Applying systems thinking to analytical system development for managing the antimicrobial resistance crisis

Abstract

Antimicrobial resistance (AMR) is an issue with foundations in clinical and agricultural sectors that has become a cross-sectoral and global human, animal, and environmental health crisis. Advances in detection technology and data sharing will allow for quick diagnostics and treatment, real-time monitoring and analysis to prevent AMR exposure, and multi-variate spatiotemporal trend analysis on the dynamic issue that is AMR to make informed interventions and policy. AMR samples are widely diverse in their matrices including blood, urine, wastewater, environmental waters, and soils. Samples can contain many interferents, particularly other contaminants like metals, microplastics, and organic pollutants, that can influence the apparent AMR. Thus, there is a need to drive innovation in cost-effective, rapid, and portable detection technologies and standardized testing methodologies for monitoring, understanding, and managing the complex AMR crisis.

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Introduction

Antimicrobial resistance (AMR) is a contaminant of emerging concern (CEC) with projections for growing impacts on environmental and human health.^1–3 This has generated global calls for international cooperation to formulate a global response to AMR. We echo this call for international, multi-disciplinary, cross-sectoral, cooperative action, including comprehensive surveillance, with recognition of the complexity and interconnectedness of AMR across sectors and geographic regions.^1–3

Antibiotic-resistant bacteria (ARB) and AMR have been an alarming issue in clinical practices for as long as antimicrobials have been synthesized en masse for clinical use beginning with the isolation of penicillin by Sir Alexander Fleming.^4–7 The race to find more antimicrobials is, however, only one branch to approach this developing crisis and alone is insufficient in response to AMR. A single antimicrobial cannot effectively target all microbes due to the differences in mechanisms of action due to their differences in biology and, as we have continuously observed, resistance will continue to arise to new agents rendering them ineffective.^7–11

A 2017 World Bank report included economic simulations showing that a low AMR impact scenario could cause a loss of over $1 trillion USD annually in global gross domestic product by 2030, and a high-impact scenario with losses or over $3.4 trillion USD annually.¹² Estimates showed, that by 2050, AMR could cause up to 10 million deaths and result in $1 trillion USD in additional healthcare costs.¹² The Council of Canadian Academies reported that AMR caused an estimated 14 000 deaths, and $1.4 billion in expenses to the Canadian healthcare system in 2018.⁹ Another estimate from 2022 suggests that a nearly 5 million deaths globally in 2019 could be associated with AMR, putting AMR in third place for the global burden of disease causing death, behind only stroke and ischaemic heart disease.¹³ Many experts and governing bodies agree that the largest health and economic impacts of AMR will be on communities living in poverty and that the poorer countries suffer the most from the global AMR crisis.^9,12,14–16

Establishing shared knowledge and terminology

The authors of this review work in multidisciplinary and interdisciplinary fields and understand that differing terminology and jargon create barriers in communication and understanding. For clarity, this section creates a shared vocabulary by defining key terms, with disciplinary context, to facilitate the discussion about AMR across the various disciplines and sectors. These definitions create a base for a shared understanding and to inform decision-making, particularly within bioanalytical experimental considerations. Firstly, classifying AMR as a CEC may be odd to our analytical community given that AMR is a developing phenomenon rather than a discrete entity. Additionally, the term contaminant in analytical sciences would refer to a specific analyte or set of analytes. In this case, specific analytes such as ARB and antibiotic resistance genes (ARGs) would be what are measured to characterize and assess the magnitude of AMR. Often, AMR is referenced to what is being detected.^17–20 As antimicrobial agents (AMAs), AMR microbes (bacteria, fungi, viruses, parasites), and ARGs increasingly entered and disseminated through the environment at the global scale, AMR has become closely associated with pollution.^1,16,21–23 AMR itself is a measurable major health concern and is used as the conventional umbrella term for the total account of AMR-related contaminants that needs to be monitored, managing the spread of, and mitigating the changing impacts of. The term “emerging” with respect to AMR as a CEC refers to the recent, emergence as a threat and the expansive transport and dissemination of AMR resulting in a changing problem that has a lack of effective implemented management strategies due to lack of understanding and lack of effective detection ability.^3,9,13,16 While AMR has existed for a long time, the increasing resistance rates, severity of infection, prevalence in multi-drug resistances, and the consequential health outcomes show that AMR and the AMR crisis continue to rapidly evolve as a critical global health and scientific concern.

Systems thinking is an approach to understanding problems by focusing on how multiple units or sub-systems interact through structured relationships (governed by societies’ policies) rather than considering each component in isolation.^24–28 Studying AMR through systems thinking recognizes that systemic socio-ecological structures affect the emergence, dissemination, and impact of AMR, and ultimately, the effectiveness of our mitigation responses.^25–28 Acknowledging the AMR crisis as a complex, dynamic, and adaptive system is simultaneously hopeful and sobering, it shows that we can affect change but also that our (in)actions have multiple implications in this interconnected system. A systems thinking approach results in multiple data sources from different sectors being integrated and analysed holistically to inform policy, prevention, and intervention. By breaking down the AMR crisis into smaller sub-systems and micro-level interactions and how these influence the broader system network, the significance of potential AMR drivers, transport and dissemination pathways can be further clarified and understood.

Furthermore, some historical and microbiological background is provided to create a similar knowledge base to include readers from various backgrounds and familiarity with AMR. This empowers all readers to fully engage in the analysis and discussion of AMR in the micro to macro context, through the lenses of bioanalytical chemistry and systems-thinking to foster an appreciation for the complexity of the AMR crisis.

This review is structured to guide readers from fundamental interactions at the microscale between AMR to systems-level considerations to improve analytical methodologies for effective and sustainable responses to the global AMR crisis. Firstly, we situate the discussion to be engaged with the One Health framework. Beginning the main discussion, localized interactions between AMR and co-occurring contaminants are examined to determine the influences co-contaminants have on AMR and on sample analysis. The discussion then expands to transport pathways through which AMR and co-contaminants move within and across interconnected One Health sectors (human, environmental, and animal and plant health), highlighting key AMR environments, as shown in Fig. 1. Readers are encouraged to refer to Fig. 1 to ground the discussions of AMR at the microscale through to sector-specific environmental analyses, as each contribute to the larger, interconnected, and complex AMR crisis. The review then connects these AMR analyses to the need for coordinated monitoring strategies, emphasizing how standardized methodologies, harmonized reporting, and cross-sector communication are needed to establish a larger integrated analytical framework and systematic interoperable databases. This enables further bioinformatics and machine learning analytics for identifying localized and global AMR trends. Finally, the paper proposes recommendations towards a comprehensive approach centred on innovative sensing technologies, robust diagnostics, and global collaboration to improve AMR surveillance, prevention, and treatment infrastructure while supporting evidence-based policy development.

Fig. 1 Schematic representation of antimicrobial resistance (AMR) dissemination across sectors of the One Health framework. Human, agricultural, and environmental sectors are connected through multiple pathways, including water, food, waste, and atmospheric processes. These interlinked routes showcase how AMR circulates across systems and affects all interconnected domains. Water is the primary transport pathway for AMR. Solid and thick arrows represent transport of AMR within sectors, dotted and thinner arrows represent transport of AMR via leaching and/or runoff, and solid thinner arrows connect AMR stressors and drivers to key environmental settings within and between sectors.

Antimicrobial resistance: emergence, mechanisms, and spread

AMR is a phenomenon broadly described as the mechanisms by which evolving microorganisms survive the antimicrobial agents (AMAs) intended to inhibit them, threatening public health.^{8,9,16,29–31} However, AMR is not a new phenomenon as microbes have been evolving in community and competition with each other for millions of years.^{4,8,10,23,31–33} For example, gram-negative bacteria with both an inner and outer membrane have better resistance to environmental pressures (like metals) and to many antibiotics that are effective on gram-positive bacteria which only have a single membrane.^10,11,34,35 Many AMAs, such as antibiotics, are natural products isolated from microbes that produce them to inhibit competitor microbes.^{4,10,22,23,30,33,36} These original AMR mechanisms by which microbes have evolved over millions of years of competition before anthropogenic interference are commonly referred to as “intrinsic resistance”. However, new interactions with new abundances of AMAs in the environment have applied selective pressures for AMR.

Humans have used plants and fungi to treat disease for millennia. The discovery of penicillin by Sir Alexander Fleming in 1928 was a result of his curiosity about a repeated observation in his Petri plates that a mould, Penicillium notatum, inhibited the growth of Staphylococcus aureus culture.⁴ Penicillin was successfully mass-produced to treat bacterial infections transforming modern medicine. However, resistance to penicillin in hospitals became apparent by the 1940s and continued to spread to bacteria strains in the larger community by the 1970s.^5,6 The discovery, isolation, mass-production, and (mis)use of subsequent antibiotics and other antimicrobials ultimately resulted in “acquired resistance” to each new drug. Microbes acquire resistance through genetic mutations or horizontal gene transfer (HGT) to combat antimicrobials, and then survive and produce progeny with such advantageous genetic mutations.^{7,10,32,37–44} Generally, mechanisms of AMR can be sorted into three categories according to the biochemical pathway associated with the resistance:^{10,11,34,35,45–49}

I. Producing enzymes that modify and inactivate the antibiotic molecule by altering it (such as by acetylation) or by destroying it (such as hydrolyzing the β-lactam ring of β-lactam antibiotics).

II. Changing the target site of the antibiotic.

III. Preventing the antimicrobial from reaching the target by inhibiting uptake or actively effluxing the antibiotic compound.

Bacteria can share DNA, including ARGs, with each other through HGT. There are three mechanisms of HGT:^{21,31,33,37,40–44,50–60}

I. Transformation – a bacterium releases DNA into their local environment and is taken up randomly by nearby bacteria.

II. Conjugation – bacteria can transfer genes to other bacteria via direct contact.

III. Transduction – genes are transferred to bacteria via bacteriophages.

These mechanisms can be further mediated by mobile genetic elements (MGEs), such as plasmids, that can obtain bacterial or other microbial DNA and move them to other cells. Sharing of ARGs becomes more efficient when bacteria are physically bound in proximity, such as in biofilms or attached to microplastics in the environment.^{37,43,44,61–65} Bacteria have co-evolved in diverse communities with other microbes (other bacteria, fungi, viruses, etc). While it is rare and the mechanisms are not fully understood, HGT has been observed between bacteria and fungi, amongst fungi, and with algae too.^40,52

Mechanisms of resistance and of HGT are important considerations for detection and surveillance of AMR, including new method development. Detection methods can distinguish ARB from other bacteria by targeting biomarkers and other molecules linked with resistance mechanisms or genes. HGT illustrates the importance of including resistance gene detection in samples where clinically associated ARB may be absent. Viral DNA and RNA, along with ARGs, have also been observed to be transported by bacteriophages viruses that infect bacteria.^{31,37,52,53,56–59} This dynamic exchange of genetic information and ARGs specifically leads to increasing amounts of ARB and AMR microbes found in the environment.

Why it is important

Historically, AMR has been regarded as just a clinical and public health issue. Mechanisms of AMR and ARGs continue to be studied to inform clinicians and develop novel pharmaceutical treatments of infections. It is important to study AMR in microbes to understand AMR mechanisms and to develop therapeutics, detection, and diagnostics technologies. Research has shown that AMR has been a growing problem in sectors such as agriculture,^8,29,66–73 clinical,^{8,37,54,74–85} and waste management^{42,62,80,84–96} and ultimately, these sectors are all sources for AMR and ARGs in the environment. AMR cannot be meaningfully addressed without using a holistic approach and studying cross-sector dynamics. These sectors are interconnected, particularly with water being a significant transport mechanism of AMR allowing movement across all sectors at both the local and international scale (Fig. 1). Due to anthropogenic activities and globalization, the impact of AMR and ARGs could be far from the source.

Combatting AMR effectively will require interdisciplinary international efforts to fully understand AMR patterns, trends, and transport. Regulators cannot create effective policies or justify resources to implement surveillance programs around AMR because there are too many unknowns due to lack of data, yet institutional support and funding is not sufficient to promote research and development. In addition, research funding differs across microbial life domains resulting in large gaps of knowledge and delayed response. For example, less than 1.5% of all infectious disease research funding is awarded to fungal infections, resulting in treatment guidelines informed by limited evidence according to a 2022 World Health Organization (WHO) report.⁹⁷

Cooperation and communication across sectors and globally can greatly improve data quality, standardize AMR surveillance schedules and techniques, create vast data sets for bioinformatics or machine learning techniques, calibrate AMR, and ultimately inform policies surrounding antimicrobial usage, clinical treatments, and AMR surveillance and management. To understand AMR trends of release, spread, exposure, and infection, we must be able to monitor AMR. To be able to monitor AMR, we require innovative development of robust and fast detection and diagnostics technologies to collect vast amounts of data. The data collection also introduces a factor where standardized methodology, and reporting units will be necessary to effectively compare and communicate findings so that professionals across all sectors globally are on the same page.

One Health framework

The One Health framework, while lacking a single, universally accepted definition, is generally understood as an integrated approach that aims to optimize the health of humans, animals, and the environment through coordinated, cross-sector collaboration. As noted by the One Health High-Level Expert Panel (OHHLEP), the term has been interpreted in different ways across institutions and the literature, which led to the development of a shared working definition to support consistent understanding and application among global partners (WHO, FAO, UNEP, WOAH).⁹⁸ Even still, different definitions arise from lack of culturally appropriate translations of the term.⁹⁹ The framework emerged from the growing recognition that health outcomes are shaped by the interactions between people, animals, and ecosystems.¹⁰⁰ While its roots can be traced back to the 19th-century work of Rudolf Virchow, who emphasized that there should be no dividing line between human and animal medicine, the framework began to take shape in its modern form in the early 2000s.^101–103 One Health gained prominence during the 2000s, particularly in response to the SARS outbreak and the spread of highly pathogenic avian influenza.^101–104 These events led to increased cross-sectoral collaboration and the development of strategic frameworks to prevent and respond to zoonotic threats.^101–104

Interestingly, One Health does not recognize nor credit the already existing deeply embodied knowledges, respect, and praxis of reciprocity and relations to the land, wherein the understanding that there are not divisions between human and animal individuals, communities, ecosystems, the environment, and mother earth has holistically served Indigenous peoples’ emotional, intellectual, physical, and spiritual health globally since time immemorial^{105–110,112} These values and knowledges are reflected in traditional teaching such as the seven generations teaching of many Indigenous nations here on Turtle Island,^107,111 and in frameworks that were co-created by Indigenous scientists and knowledge keepers such as traditional ecological knowledge,^100,107 two-eyed seeing (in the Canadian context),^113,114 and both-ways (or two-ways, in the Australian context).¹¹⁴ Centring and ethically collaborating with Indigenous knowledge keepers with intention and commitment to reciprocate knowledge and solutions can strengthen relationships and enrich Western science frameworks like One Health. One example where Indigenous knowledge keepers’ local ecological knowledge can improve analytical methodologies in the context of monitoring and managing the AMR crisis is in the selection of AMR monitoring sites and in changes to AMR sampling frequency and/or sample types (water, soil, etc.) due to factors such as animal activities. Other highly desired and aligned to One Health outcomes of including Indigenous ways of knowing into One Health would be prioritizing locally practical solutions and increased engagement and commitment by local community members¹¹⁵ in healing major One Health sectors (human, animal, environment) through active participation in scientific exploration and applying solutions. Participation in shared creation and management of knowledge also builds trust and brings in new voices when applying knowledge, particularly important on an application and policy level for tackling AMR. Indigenous communities rightfully have distrust in western health systems which can be alleviated by allowing participation and weaving newly created knowledge with existing Indigenous knowledge frameworks with proper recognition.

AMR has traditionally been treated as a clinical problem, with most efforts focused on the misuse and overuse of antibiotics in healthcare settings.^8,82 However, this limited view has shifted as growing evidence highlights how resistant pathogens and genes move between humans, animals, and the environment.^1,16,22,116 Wastewater discharge, agricultural antibiotic use, direct animal-human interactions, and global trade have shown that AMR is not only a clinical concern but a systemic one.^1,3,101,117 Water, considered part of the environmental domain within the One Health framework, plays a critical role in linking human, animal, and ecosystem health. It acts as a carrier for nutrients, pollutants, and microscopic particles, and provides a microbial habitat that enables the dissemination of ARGs and AMR microbes.^{7,39,61,62,92,118–121} Framing AMR through a One Health lens allows for more coordinated and sustainable solutions. It shifts focus from isolated, clinical interventions to broader strategies that address upstream drivers such as pharmaceutical pollution, agricultural practices, and weak AMR surveillance systems.⁹⁸ The widespread influence and increasingly alarming consequences of AMR is not limited to health implications but also to economic stability and social equity at the global scale. Thus, AMR can also be viewed in the context of the United Nations Sustainable Development Goals (SDGs) highlighting that AMR is both a scientific and societal challenge.

Detection and monitoring AMR in these various contexts requires an informed consideration of AMR interaction with smaller and more manageable components of these larger systems. Understanding that localized interactions of AMR to other individual contaminants will affect their emergence, AMR mechanisms, and dissemination. This can further be expanded to understand how cumulative effects of multiple contaminant interactions could influence AMR within larger environmental and sectoral contexts that have their own unique influences and sample types. Ultimately, having a better understanding of the effects on AMR of contaminants, other interferents, and matrices that make up the components of a ‘real’ sample will help create sensing methodologies and technologies with more targeted mechanisms.

AMR community interactions with other contaminants

Many studies on AMR are completed either in laboratories or with isolated microbes that were culturable from environmental samples. The understanding of the biochemical mechanisms of AMR in isolated microbes in clean conditions provides a foundation for researching how AMR interacts with other contaminants and other microbes in the environment. Development of detection methods must consider aspects of “real world” samples because they may affect multiple aspects of detection from extraction and availability of biomolecule targets to presence of interfering components in the sample matrix. Table 1 at the end of the section summarizes the interactions of AMR with the following contaminants discussed.

Table 1 Summary of contaminant Interactions with AMR

Contaminant	Influence on AMR	Ref.^a
a Non-exhaustive list.
Biofilms	Facilitates HGT, AMR reservoir, reservoir for contaminants like metals and microplastics	43, 44, 61–63, 65, 120, 122 and 123
Metals	Co-selective pressure, binds DNA, damages DNA, increases HGT	39, 61, 72, 124, 128, 130, 194 and 195
Microplastics	Adsorbs DNA, damages DNA, AMR reservoir, biofilms reservoir, transports biofilms, increases HGT, increases expression of efflux pumps	61, 63, 138, 145, 146, 148, 153 and 155
PCPs	Co-selective pressures, damages DNA, increases HGT, increases expression of efflux pumps	49, 50, 159, 161, 163, 165 and 170
Pesticides, herbicides, VOCs, PAHs, and other organic contaminants	Co-selective pressures, binds DNA, damages DNA, increases HGT, increases expression of efflux pumps	175, 177, 180, 184, 186–188, 191 and 196

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Biofilms

Biofilms are surface-attached microbial communities that are embedded in a matrix of extracellular polymeric substances (EPS) that are excreted from the microbes.^122,123 Most natural environmental microbial life and communities exist in/as biofilms as well as in some engineered biotechnology environments. Biofilms are found everywhere from within both natural and built aquatic systems (e.g. rocks in a river, drinking water pipes) or to touch surfaces (e.g. countertops), and into the human body (e.g. dental plaque). Biofilms can be very diverse and spatially heterogeneous in microbial, macromolecule, and abiotic composition.^{61,63,64,122,123} The structure and composition of the biofilm are dependent on the environment that the biofilm is in and the microbial composition which may contain single or multiple microbial species. The EPS matrix is also variable in composition of macromolecules as well as environmental organic and inorganic materials like environmental DNA (eDNA), metals, and/or microplastics that can be incorporated into the biofilm when they adsorb onto them.^{43,44,61–64,120,122–126} The spatial composition of biofilms can also create various microenvironments within biofilms, which can alter the microbial community within that microenvironment. Smaller pieces of mature biofilms can shed off from forces of water, travel potentially large distances, and establish a biofilm elsewhere.^61,122,123

Biofilms are microenvironments that can facilitate microbial community dynamics such as nutrient exchange, cell signalling (i.e., quorum sensing), cooperation and competition, and HGT. All mechanisms of HGT can be found in biofilms due to their retention of DNA and MGEs in the EPS, having cells in close proximity, general environmental stability resulting in prolonged survival, and interactions such as genetic exchange, and proliferation of diverse microbes. Genes from the environment, including ARGs, can also adsorb to and be absorbed into biofilms.^{43,44,62,64,65,122–124} Biofilms can thus be hotspots for ARGs and can result in multi-drug-resistant microbial communities.

Metals

AMR co-occurs with metals and other heavy elements in our environment, particularly in the soils and waterways. Currently, the anthropogenic levels of metals in these environments are several orders of magnitude greater than the levels of antibiotics, and unlike antibiotics, metals are not subject to degradation.^127–129 Concerningly, even when there are no AMAs present, certain metals (Pb, Cu, Cr, Zn, etc.) can maintain or increase bacterial resistance by exerting long-term selection pressure.⁷² For example, Zhang et al. (2018) found that sub-inhibitory levels of these metal ions (similar to those found in polluted environments) can accelerate the HGT of ARGs, as the presence of metal ions significantly up regulated the expression of ompA and ompC genes, which are associated with transcription.¹³⁰ Thus, it could be useful to monitor for these transcription related genes in samples where metals are known to be present.

Not only do metals exert selective pressure for antibiotic resistance, the presence of metals within the sample matrix have been shown to interfere with bacterial detection. A common technique used for AMR detection, PCR, has been shown to face inhibition due to metals at various points during DNA analysis including extraction and PCR amplification, as positively charged metal ions have a high affinity for DNA due to its negatively charged phosphate backbone.^131–135 Sagripanti et al. (1991) found that Cu specifically, has high binding affinity for the DNA bases.¹³⁶ The level at which metals hinder PCR analysis is largely dependent on the concentration and type of metals involved.¹³² For example, Wedrychowski et al. (1986) found that Hg, Cu, Pb, and Al produce extensive crosslinks between DNA and proteins, which can inhibit PCR by blocking access to the DNA template.¹³³ In another study, Kuffel et al. (2020) found that Zn, Sn, Fe(II) and Cu ions have the greatest potential to interfere with DNA amplification with 50-+% PCR inhibition at concentrations of 0.26, 0.31, 0.59 and 0.77 mM respectively.¹³²

Nano- and microplastics

Nano- and microplastics are ubiquitous environmental pollutants that can interact with microbes such that they can facilitate the development and spread of AMR, such as via growth of biofilms on their surfaces. The high surface areas of microplastics are ideal for biofilm formation and biofilms are known to facilitate HGT including ARGs.^{10,61,63,122,137–145} These microbial communities, potentially reservoirs for AMR, are transported wherever the microplastics are transported. Water can transport the biofilm-carrying microplastics large distances and through various ecosystems effectively spreading AMR.^{7,16,22,137,139,140,143,144,146,147} This can be of great concern for microplastics passing through environments such as wastewater treatment plants (WWTPs) which are already hotspots for AMR.

Microplastics also allow the transport of biofilms into and up the food web through ingestion. Nanoplastics have also been observed to enter microbes through the lipid bilayer or via endocytosis – these will enter the food web if these microbes are ingested.^{137,140,144,146–152} Absorption of nanoplastics into cells can influence and change microbial populations because they can create oxidative stress in cells, causing oxidative damage to DNA that can lead to mutations. Consequently, cells undergo SOS response with upregulated expression of core conjugation genes, facilitating HGT including any ARGs.^148–154 Finally, microplastics can adsorb and transport other contaminants such as antibiotics and metals synergistically with DNA and cells, which can create microenvironments of selective pressures on any attached microbes.^{61,138,140,143,148,155} These influences of nano- and microplastics on AMR creating ARG-rich biofilms could result in increased ARG detection within environmental samples containing higher amounts of microplastics. While this possible overestimation could be considered as a conservative estimate, it would introduce a bias and may misrepresent the patterns and trends of AMR and ARGs at a monitoring site determined using that inaccurate data.

Association of ARB and ARGs with nano- and microplastics, including in biofilms on plastics, can interfere with the detection of biomarkers and other targets. If the analyte is eDNA, where in an environmental sample may contain various abundances along with metal ions, nano- and microplastics, the eDNA can be damaged or adsorbed, consequently affecting any genomic analyses.^61,152,155 Any environments that are reservoirs for nano- and microplastics must be studied in how the microplastics are transported as well as how they may be potential reservoirs for ARGs and AMR.

Personal care and pharmaceuticals products

Personal care products (PCPs), including cosmetics, soaps, shampoos, lotions, toothpastes, and disinfectants, also play a significant role in the prevalence and spread of AMR. Many PCPs exert selective pressures on microbial communities in the environment, including domestic and commercial environments, largely because they contain AMAs to inhibit microbial growth and extend PCP shelf life.^156–163 Generic use of PCPs results in the component compounds, including AMAs and surfactants, entering the wastewater system. These compounds are not fully removed by the current wastewater treatment processes and are released into the environment. For example, triclosan and triclocarban are AMAs and preservatives found in thousands of PCPs that have shown to persist in the environment, contribute to AMR, and bioaccumulate in the food web.^{156–160,164–169} A 2007 mass balance assessment found that 1.1 × 10⁵ to 4.2 × 10⁵ kg of triclosan are discharged into the environment by WWTPs in the US annually.¹⁶⁶ Triclosan has been found to accumulate in anaerobic digestors, WWTPs sludge, and aquatic ecosystem sediments, and has been observed to influence the microbial communities, promote HGT, cause genetic mutations, and select for resistant microbes.^{54,157,159,160,163–165,169}

Other PCP preservatives such as sodium nitrite and sodium benzoate have shown to increase HGT by increasing the expression of trfAP and traJ genes by up to 17-fold in a concentration-dependent manner.¹⁶⁹ Another major component of PCPs are surfactants, such as benzalkonium chlorides (also classified as a quaternary ammonium compound antiseptic), can select for AMR through multiple mechanisms including mutagenesis, upregulated expression of multidrug efflux pumps, and changing membrane fatty acids to modify cell surface charge.^49,170 Upregulation of different efflux pumps has been a prominent observed resistance mechanism to antiseptics.⁴⁹ LaBreck et al. (2020) determined that pre-exposure to one antiseptic can increase resistance to another antiseptic suggesting a co-selection for multiple resistances.⁴⁹ Other non-antibiotic pharmaceuticals, including the commonly prescribed antiepileptic carbamazepine, significantly promote HGT and thus the dissemination of ARGs.^50,54,163 The commonly prescribed antidepressant fluoxetine has been found to induce AMR in Escherichia coli (E. coli) to multiple antibiotics via genetic mutation and upregulated expression of multidrug efflux pumps.¹⁷¹ The microbial communities within the WWTPs and in the environment are continuously exposed to these PCPs and the selective pressures they exert, promoting the proliferation of AMR.^{80,81,163,172}

Pesticides and volatile organic compounds

Pesticides, including herbicides, fungicides, etc, are chemicals used largely in agriculture but also in urban areas to deter, control growth of, or eliminate unwanted pests and plants.^173,174 The concern for environmental pollution of pesticides and herbicides, largely from agricultural runoff, may be more consequential given the unintended effects of promoting AMR.^{67,70,173–182} Exposure to these chemicals can increase the abundance of ARGs in soil microbiomes by triggering cell defence mechanisms including upregulation of multidrug efflux pumps and enhanced HGT.^{48,67,174,175,178,182,183} The altering of microbes and microbiomes via herbicides and pesticides not only alters human pathogens but also in phytopathogens that are directly responsible for the loss of agricultural quality and yield.^173,184,185 Glyphosate, a widely used herbicide against weeds and a main chemical in Roundup, has demonstrated that its use and exposure promotes AMR particularly through the increased mobilization of ARGs and MGEs, upregulated expression of efflux pumps, and/or reduced expression of outer membrane porins.^{67,174,175,178} Rangasamy et al. (2018) determined that microbes collected from agricultural fields that were resistant to organophosphate pesticides were also resistant to antibiotics because α–β hydrolase enzyme that biodegrades the organophosphate pesticides are capable of degrading antibiotics.¹⁸⁰ These findings suggest that pesticides can exert a co-selection pressure for AMR and pesticide resistance.

Other organic environmental stressors include volatile organic compounds (VOCs), solvents used at industrial scales, and polycyclic aromatic hydrocarbons (PAHs). Complex interactions with these organic pollutants result in differing effects on AMR development. Bacterial tolerance to solvents, like toluene and ethylbenzene, involve efflux pumps which have been shown to simultaneously remove multiple antibiotics, like ampicillin and tetracycline.^186,187 Bacteria able to biodegrade VOCs and PAHs have also been shown to have increased AMR to multiple antibiotics, again through the mechanism of efflux pumps and show increased MGEs.^{179,182,188–190} These studies suggest multi-resistances and co-selective pressures for these organic pollutants and AMR.

Environmental soil or water samples can contain pesticides, herbicides, PAHs, and/or VOCs that can affect genomic analyses. Several possible effects on the detection and quantification of ARGs in environmental samples include damaging nucleic acids, inefficient extraction of eDNA/RNA extraction due to adsorption to the soil, the organic interferents being extracted along with eDNA/RNA, inhibiting PCR via nucleotide adsorption, and/or inhibition of DNA polymerases.^191–193 These effects can produce false negatives due to the apparent low abundance of ARGs.

Together, these studies illustrate how co-contaminants could interfere with sample analysis or co-select for AMR. Taking these effects into consideration can help better interpret AMR results, consider sample pre-treatment, and/or select target AMR analytes such as ARGs or efflux pumps in contexts where co-contaminants may be highly suspected to be in the sample. Another consideration could be to include multi-analyte analysis that couples chemical and microbiological measurements to interpret AMR results alongside these co-contaminants and co-selective pressures. Including multi-analyte data can further determine if remediation of co-selective AMR drivers would need to be considered during an intervention.

Common possible forms of AMR introduction

Biofilms and microplastics

As discussed in the previous section, biofilms and microplastics each have unique interactions with AMR. Biofilms can be hotspots for ARGs and can result in reservoirs of AMR and ARGs.^10,197 Biofilms also offer a transport pathway for AMR and ARGs as mature biofilms can often split off pieces that can travel via water to a different location where the biofilm can continue to form and proliferate.^{1,43,44,61,62,64,65,122,123} Biofilms often form on microplastics, easily colonizing the high surface area substrate, in both aquatic and terrestrial environments.^{137,138,140,141,143,144} These microplastics can be transported through various mechanisms, such as with water or in air, ultimately transporting the attached biofilms with them. For example, AMR and ARGs could enter the food chain when fish consume microplastics that have these biofilms on them.^{137–140,143,144,146} Ultimately, the transport of microplastics is of great concern with respect to AMR because AMR may go wherever the colonized microplastics go. This could mean transporting and introducing AMR to new ecosystems, new microbial communities, and into the food chain.

Bioaerosols

A phenomenon that is common yet did not receive much public attention prior to the SARS-CoV-2 pandemic is a bioaerosol.^198,199 Bioaerosols can be composed of various living and dead microbes (bacteria, fungi, viruses, archaea, etc) including any spores, pollen, or fragments of cells debris.^200,201 Bioaerosols are consistently generated from various natural sources such as from soil surfaces, water bodies, and animals.^200–203 Anthropogenic activities are also a major source of bioaerosols such as in the collection, transport, and landfilling of wastes, wastewater treatment, agricultural practices, and more.^{23,68,202,204–209} Weather events such as wind and rain can facilitate the generation and transport of bioaerosols.^206,210 It has been reported that 0.01% of bacteria from soil could aerosolize when a raindrop hits the soil surface, dispersing microbes into the air.²⁰²

Microbes in bioaerosols can be transported by the wind and can deposit via gravity or by precipitation.^{201,206,210,211} Depending on climate conditions, bioaerosols can travel large distances through different conditions and the microbial composition and particle composition may change; this also applies to small and/or indoor spaces.^{162,206,207,211–213} Studies have shown that dusts of bioaerosols can remain in the atmosphere for a long period of time, being able to compositionally change with already aerosolized microbes resulting in high microbe diversity. A metagenomic study on samples collected during a Nov–Dec 2014 dust event in Seoul, South Korea, where 16S rRNA genes sequenced via pyrosequencing revealed that there was high bacterial diversity showing 19 phyla and further characterization found 646 genera present.²¹⁴ Another Asian dust event study by Tang et al. (2018) showed their samples contained 34 bacterial phyla (further characterized to 243 families) and 3 fungal phyla (further characterized to 149 families).²⁰⁶ Placing this into an agricultural context, Bai et al. (2022) showed that microbes and their ARGs in bioaerosols could be detected 10 km away from animal farms.⁶⁸

Migration and AMR mobility

Organisms at all levels of complexity, including carriers of AMR organisms or ARGs, rarely remain confined to one single space or ecosystem. Local travel and travel across landscapes and borders both in natural and human-mediated processes occur continuously, transporting the AMR organisms and ARGs.^{2,183,215–217} Many wild animals have deeply innate migratory patterns tied to their life cycles and survival.^218,219 These animals can act as mobile AMR reservoirs acquiring ARB and AMR from contaminated sites, such as from landfills and WWTPs, and potentially dispersing the AMR over large distances during seasonal migrations. Evidence has shown that gulls foraging in landfills can spread ARB to nearby areas and potentially across continents during their migratory season.²¹⁷ One can imagine that geese swimming and feeding in wastewater effluent ponds acquire ARB and subsequently fly and introduce AMR to urban parks and recreational waters. The recent ongoing zoonotic pandemic of SARS-CoV-2 is a great example to show the transmission of disease goes both ways with human disease variants transmitting to animals from white tailed deer in remote hunting grounds that been found with SARS-CoV-2 infections to domestic animal friends to wildlife under human care like in zoos.^220,221 Anthropogenic activities on local and larger international scales also contribute heavily to AMR spread. Local communities can be affected by hospital effluent discharges or due to proximity to landfills. Simultaneously, natural weather processes such as precipitation, wind, and worsening natural disasters also carry AMR microbes and ARGs, especially from agricultural lands/run offs and sewage overflows, further disseminating AMR into both adjacent and faraway environments.^{32,64,70,88,141,145,163,176,202,206,210,211,222–226} On a global scale, international travel, trade, and mass migrant or refugee migrations can transport AMR into new areas potentially seeding and diversifying AMR genes.^{15,215,224,227–229}

One key common factor is the role that water plays in the transport and mobility of AMR. As water moves and flows around the globe in the forms of the environment, weather, and as human and animal bodies, AMR moves with no regard for borders or boundaries.^{7,32,62,66,119,121,202,204,215,216,218,219,230–238} The interconnected pathways of AMR dissemination results in the need for a global collective intervention. No single region can eliminate AMR in isolation as limited efforts will be undermined by the continual reintroduction of AMR through the movement of water and anthropogenic activities. The One Health framework provides a method for a coordinated international response to the complex interconnected systematic issue that is AMR.

Standard and emerging methods of AMR detection and characterization

Methods for AMR detection can be categorized as genotypic, focusing on detecting nucleic acids, and as phenotypic, where conventional culturing and bioassays are performed.^222–227 Genotypic methods give us information about the presence and prevalence of ARGs. Phenotypic methods give us information about what AMR mechanism the microbe is using and what ARGs are expressed.^20,239–243 Currently, the standardized AMR detection methods used in testing are the phenotypic Kirby-Bauer disk diffusion method, broth microdilution, Epsilometer test (Etest), and the genotypic polymerase chain reaction (PCR) – specifically, quantitative PCR (qPCR), sometimes called real-time PCR, is largely used in AMR detection.^{20,239,244–248} However, only some antibiotics and bacterial species have been standardized with the disk diffusion method and the meaning of an inhibition zone for a new antimicrobial or different bacterial species would need to be repeatedly validated.^239,245,246 The disk diffusion method also cannot determine the minimum inhibitory concentration (MIC) of the antibiotic like the broth microdilution method.²³⁹ Both methods are constrained by the growth time of the bacteria, do not determine the mechanism of AMR, and need to start with an isolated culture, making them not suitable for fast, reliable testing. Newer genotypic methods such as PCR are faster and useful for identification of ARGs, but still require sample preparation by highly trained personnel in a sterile lab facility; qPCR is also an option, but these instruments are costly and still constrained by the same PCR factors.²³⁹ Another issue with PCR testing is that the presence of ARGs does not mean that the bacteria are expressing them, and we cannot know if those bacteria are actively ARB. These methods cannot currently be used for rapid, routine testing of ARB nor for on-site monitoring, especially in remote areas. Development of a portable, in situ testing device would avoid the need for expensive analytical instruments and highly trained personnel.

Researchers developing new methodologies for AMR detection use at least one of the standardized genotypic or phenotypic methods to compare and validate their methods. However, it is valuable to conduct both genotypic and phenotypic methods to screen for ARGs, identify microbes (such as using 16S rRNA to identify bacterial species), and to confirm the AMR phenotypes and mechanism(s). Abandoning culturing and phenotypic methods too quickly in favour of relying on only genotypic tests will result in gaps of data and making misinformed decisions. The correlation of genotype to phenotype is still an interpretive challenge due to various reasons such as gene expression, gene regulation, and post-translational modifications on proteins. Advances in predicting AMR phenotype from ARGs is appealing, but current machine learning models still require complete genome sequence data and results have high error rates.^249–253 It is important to continue to collect large amounts of genotypic and phenotypic data using current and new methods to be incorporated into open databases to ensure data is aligned and validated as new technologies emerge. Creating these large, validated, open datasets produce better training sets resulting in continued advances in integrating machine learning and artificial intelligence for AMR predictions, diagnostics, and spatiotemporal trend analysis. An overview of standardized AMR detection methods along with new emerging methods and technologies, such as biosensors, that have potential to be robust and reliable for AMR detection are presented in Table 2. In depth reviews on the standard and recent advancements in detection methods at various technology readiness levels are abundantly available elsewhere and within the referenced literature.^{17–21,239,254–256}

Table 2 Current and emerging AMR detection methods

Technique name	Description	Sample types and processing	Current application maturity and limitations	Time needed^a	Example references^b
a Time of the detection technique, does not include obtaining bacterial isolates in which 18–48 hours is added to the testing time.b Non-exhaustive list.
Genotypic methods
Conventional polymerase chain reaction (PCR)	Amplifies specific DNA sequences within a sample using primers for a target gene including ARGs.	Clinical isolates, environmental samples (soil, water, air, sediment); DNA extraction, isothermal amplification	Widely used, established public health use; pre-processing samples, targeted nucleic acid sequences only	2–4 hours	59 and 260
Real-time PCR (qPCR)	Real-time detections and quantification of target genes.	Clinical isolates, clinical samples (blood, urine); DNA/RNA extraction, isothermal amplification	Widely used, established public health use; pre-processing samples, targeted nucleic acid sequences only	1–4 hours	213 and 261–263
Multiplex PCR	Amplification of multiple target genes simultaneously in a single reaction.	Clinical samples (blood, urine); DNA extraction, multiple primer optimization, isothermal amplification	Emerging commercial products for clinical use, AMR research; pre-processing samples, targeted nucleic acid sequences only	2–4 hours	248, 264 and 265
Digital PCR (dPCR)	Absolute quantifications of nucleic acids by partitioning samples to detect and quantify target genes with high sensitivity.	Clinical isolates, clinical samples (blood); DNA extraction, isothermal amplification, partitioning	Used in AMR research, limited but growing clinical use; cost per test, pre-processing samples, targeted nucleic acid sequences only	2–4 hours	266–268
Reverse transcriptase (RT-PCR)	Transcribes RNA into cDNA before amplification; can identify reproducing cells with high sensitivity, making it useful for detecting live ARB.	Clinical samples; RNA extraction, reverse transcription	Advanced research studying expression of mechanisms, sometimes used for diagnostics; pre-processing samples, targeted nucleic acid sequences only	3–5 hours	269 and 270
Whole genome sequencing (WGS)	WGS provides comprehensive genomic information and can potentially detect all AMR-encoding genes in a single assay	Bacterial isolates; library preparation, DNA extraction	Established in public health use and reference labs, emerging clinical use; pre-processing samples, cost, bioinformatics expertise and infrastructure	24–48 hours	55, 75 and 241
Short read sequencing	Technologies like Illumina generate short DNA fragments that can be assembled or directly analysed to predict resistance.	Clinical isolates, environmental samples (soil, water, sediment); library preparation, DNA extraction	AMR research and in labs for AMR profiling, uncommon diagnostics use; pre-processing samples, library preparation, more complex data analysis and interpretation	12–24 hours	271–273
Long read sequencing	Platforms like Oxford nanopore technologies can generate longer reads, allowing for better assembly of complex genomic regions.	Clinical isolates; library preparation, DNA extraction	AMR research, emerging real-time uses; pre-processing samples, library preparation, more complex data analysis and interpretation	8–24 hours	272–274
Hybrid sequencing	Combining short and long-read technologies can provide more accurate and complete genomic information for AMR prediction.	Clinical isolates; library preparation (long and short), DNA extraction	AMR research and reference labs; cost, pre-processing samples, library preparation, more complex data analysis and interpretation	24–72 hours	273, 275 and 276
Amplicon sequencing	Specific PCR-amplified genes or regions of interest, the amplicons are sequenced to detect mutations, copy number variations, etc.	Clinical isolates; library preparation, DNA extraction, isothermal amplification	AMR research, emerging public health use; cost, pre-processing samples, library preparation, targeted nucleic acid sequences only, more complex data analysis and interpretation	4–8 hours	51 and 277
Metagenomic sequencing	Detection of AMR genes in complex microbial communities without the need for culturing.	Environmental samples (soil, water, air, sediment), wastewater, wastes; library preparation, DNA/RNA extraction	AMR research, growing public health use; library preparation, cost, bioinformatics expertise and infrastructure, more complex data analysis and interpretation	8–24 hours	82, 90 and 205
RNA sequencing (RNA-Seq)	Provides information on gene expression levels and transcriptional responses of microbes to AMAs, identifying active AMR mechanisms.	Clinical isolates; library preparation, RNA extraction	AMR research; cost, more complex data analysis and interpretation	12–24 hours	50, 277 and 278
Pyrosequencing	A real-time sequencing method where a pyrophosphate is released and starts a cascade of enzymatic reactions when the tagged nucleotide is added and generates a light signal.	Clinical isolates; DNA extraction, DNA sequencing	Could be used but largely replaced by newer technologies; shorter read length, limited materials due to disappearing use	4–8 hours	214, 279 and 280
CRISPR-Cas13	Binds to target RNA and uses collateral cleavage activity to generate and amplify signals.	Clinical isolates, bacterial isolates, clinical samples (blood, urine); DNA extraction, library preparation, isothermal amplification, CRISPR reaction	AMR research, successes in proof-of-concept research; pre-processing samples including amplification, no standardized, routine methodology yet	45 mins–2 hours	281–283
Machine-learning enhanced sequencing	Integrating AI to WGS or multiplexed sequencing can uncover new determinants of AMR or predict MICs of ARB based on their AMR profiles. Must be careful of risks of bias in the algorithms.	Clinical isolates, clinical samples (blood); DNA extraction, DNA sequencing	AMR research and proof-of-concept; requires large, high-quality datasets, lack of regulations and standardized methodology	<1 hour	251, 284 and 285
Electrochemical biosensors	A biorecognition element binding a target analyte creates an electrochemical signal. Label-free systems. Can be integrated with microfluidic devices; Ex. An electrode modified with the complimentary DNA probe of an ARG of interest, the electrochemical signal will change with hybridization of the ARG.	Clinical isolates, bacterial isolates, clinical samples (urine, blood), environmental (water); cell lysis, direct electrochemical measurement of nucleic acids	Advanced research with multiple proof-of-concepts, some real-sample success, some prototypes; no standardized, routine methodology yet, detection robustness challenges	30 mins–2 hours	286–288
Optical biosensors	Fluorometric or colourimetric biosensors, can be integrated with smartphone cameras; surface plasmon resonance (SPR) detects real-time changes in refractive index upon target analyte binding, typically gene hybridizations.	Clinical isolates, bacterial isolates, clinical samples; filtration, DNA extraction	Advanced research with multiple proof-of-concepts, some real-sample success, some prototypes; no standardized, routine methodology yet, limited miniaturization due to optical components		289–291
Phenotypic methods
Kirby–Bauer disk diffusion	Bacterial susceptibility to antibiotics and AMAs by observing and measuring growth inhibition zones around antibiotic disks.	Bacterial isolates; inoculation and culturing plates	Long-established gold standard, routine, reference method; requires isolates, long incubation times	16–24 hours	236 and 246
Epsilometer test (Etest)	Follows the principles of the Kirby–Bauer except a gradient strip of AMA is used to determine the MIC for a bacterial isolate.	Bacterial isolates; inoculation and culturing plates	Widely used; requires isolates, long incubation times, limited validated AMAs	16–24 hours	78 and 247
Broth microdilution	Determines MIC for antimicrobials by assessing inhibition of bacterial growth in liquid medium with serially diluted antimicrobials.	Bacterial isolates; inoculation and culturing of nutrient broth	Gold standard, routine, reference method; requires isolates, long incubation times	16–24 hours	241, 292 and 293
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) Mass spectrometry	Rapid bacterial identification and AMR detection through protein mass spectra analysis.	Bacterial isolates; cell lysis, protein extraction, matrix application	Established, routine method in some clinical labs and hospitals for identifying microbial species, advanced research for AMR mechanism profiling; specialized equipment, cost, pre-processing samples, no standardized AMR mechanism identification workflows yet	2–4 hours	294–296
Electrochemical biosensors	Electrochemical detection of AMR enzymes (β-lactamases, carbapenemases) and resistance proteins. Can be integrated with microfluidic devices and smartphone devices.	Clinical samples (blood, urine); bacterial isolates, environmental samples (water); direct electrochemical measurement	Advanced research with multiple proof-of-concepts, some real-sample success; fouling by non-specific interactions in real samples and complex matrices, no standardized, routine methodology yet	30 mins–3.5 hours	297–300
Optical biosensors	Fluorometric or colourimetric biosensors, can be integrated with smartphone cameras; SPR detects real-time changes in refractive index upon target analyte binding and bacterial responses to antimicrobials.	Clinical isolates, clinical samples (blood, urine), environmental samples (water); filtration, addition of indicator molecule	Commercialized fluorometric and colourimetric products, SPR in advanced research stages; scattering effects, instrument costs, limited miniaturization due to optical components	1–6 hours	301–303
Piezoelectric biosensors	Quartz crystal microbalance (QCM) biosensors report changes in frequency signals when target analyte or probes are bound to the biosensor.	Clinical isolates, bacterial isolates; filtration	Successes in proof-of-concept research; specialized instrumentation, sensitive to matrix effects requiring pre-processing samples	20 mins–4 hours	304 and 305

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Dedicated validation and standardization efforts, with various sample types, must be pushed on exciting emerging portable methods to advance AMR detection and surveillance capability and capacity. Achieving validated, reliable, robust, and portable methods has become urgent to implement effective AMR monitoring and surveillance. When validated, easy-to-use, portable AMR testing technology is developed, citizen science becomes another approach to educate and include the public which can encourage AMR stewardship.^257–259 When looking at AMR research in different sectors, the analytical methods used to detect and study AMR are the same even though the sample types are very different, requiring different sample collection methodology and, sample preparations, and may be limited to certain techniques. New innovations in technologies must consider the interconnected nature of the AMR crisis across sectors and the ability of new methodologies to reliably analyse AMR across various complex sample matrices.

Overview of AMR detection in diverse samples to support a systems approach

AMR manifests across a variety of biological and environmental systems, each shaped by distinct pressures, microbial communities, and patterns of antimicrobial use. These interconnected domains form key hotspots for the development, persistence, and spread of resistance.⁹⁴ Due to the diversity in sample sources – ranging from human and animal health to natural and engineered environments – the underlying complexity and connectivity of AMR demands an integrated approach to analysis to support a systems approach to managing AMR. Interpreting this complexity requires working across disciplines and making sense of large, heterogenous datasets.⁹⁵ While many of the analytical tools used to detect and study AMR, such as culture-based methods, molecular assays, and metagenomics, are consistent across fields, their application is highly dependent on the sample type, requiring different preparation protocols.⁹⁶ This intersection of shared methodologies and domain-specific challenges underscores the importance of coordinated efforts in AMR research and surveillance.

Clinical

Clinical settings act as intense source points in the broader landscape of AMR, placed within a web of interactions that spans human, animal and environmental systems.³¹ The routine use and overuse of antibiotics in hospitals, combined with factors like invasive procedures and patient susceptibility, creates conditions where resistance can emerge rapidly and spread efficiently.²⁹ The rise and spread of resistant bacteria in hospitals is not confined to any one region or health system, it poses a transnational threat with consequences that cut across social and economic factors.³⁰⁶ Within these environments, the high frequency of antimicrobial use, combined with the presence of immunocompromised individuals and complex medical interventions, creates ideal conditions for resistant organisms to thrive.²⁹ The consequences place a substantial burden on healthcare systems, with increased infection rates, higher morbidity and mortality, extended hospital stays, and rising demand for specialized care.³⁰⁷ In Canada, resistant bacterial infections currently impose an annual cost of $1.8 billion on the healthcare system, a figure expected to rise to over $8 billion by 2050 without intervention.⁹ In a multicentre cohort study of 12 000 patients across 66 countries, 21.6% of gastrointestinal surgical site infections were caused by organisms resistant to the prophylactic antibiotics administered perioperatively. The proportion of resistant infections varied substantially by national development level, ranging from 16.6% in high, 19.8% in middle, to 35.9% in low-income settings, highlighting significant disparities in the effectiveness of standard surgical prophylaxis globally.^236,308 Beyond economic strain, the persistence of resistant pathogens fundamentally threatens the viability of medical procedures that rely on effective prophylaxis, from chemotherapy to organ transplantation.²¹ In a 10-year study conducted in France between 2001 and 2010, 4.1% of the 710 liver transplant recipients were found to be colonized with extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae prior to transplantation. Within four months postoperatively, 5.5% of patients developed ESBL-related infections, with intra-abdominal infections being the most common type of infection.^309,310 In 2022, the WHO identified 19 fungal pathogens as priority for research and development to address the rising fungal AMR public health threat.⁹⁷ The WHO lists yeasts Candida auris and Candida albicans as two of four fungi identified as a critical priority, both of which have shown multi-drug resistance.⁹⁷ As resistant infections continue to challenge infection control practices and therapeutic protocols within hospitals, understanding their origins, transmission pathways, and persistence in clinical settings remains essential for guiding effective interventions.

Wastewater

Wastewater systems represent a critical link between human activity and the environment, and they play a central role in the mobilization and dissemination of AMR.⁹⁰ Antibiotics and ARB, along with ARGs, enter wastewater through multiple pathways. In clinical settings, hospitalized patients often receive doses of broad-spectrum antibiotics that lead to the low excretion of both unmetabolized compounds and resistant bacterial populations via urine and feces.⁸⁵ These residues and microbial communities enter hospital wastewater streams, contributing significantly to the overall AMR burden in municipal systems.^80,81,311 Surveillance data from France reflect this clinical-environmental interface, where the incidence of ESBL-producing Enterobacteriaceae in hospital settings increased fourfold between 2002 and 2013. During this period, E. coli became the predominant ESBL-producing species, rising from 19% to 59% of isolates. In parallel, community-based data indicated that ESBL-producing E. coli accounted for an estimated 3–5% of urinary tract infections, with approximately 5% of adults and children identified as asymptomatic carriers by 2011.^84,312–314

In parallel, communities contribute to the resistome through outpatient antibiotic use. Antibiotics taken at home – often incomplete or prescribed unnecessarily – are similarly excreted, and their residues, along with the resistant bacteria selected in the gut microbiota, enter the domestic sewage system.^315,316 As a result, wastewater systems accumulate inputs from both clinical environments and broader community-level antimicrobial use, making them hotspots for AMR gene exchange and selection under sub-inhibitory antibiotic concentrations.^42,94,317

WWTPs are not currently designed to target or remove antimicrobial compounds or resistance genes.⁹³ Conventional treatment processes, including primary sedimentation, activated sludge, and secondary clarification, are effective at reducing organic load and pathogens to meet general effluent quality standards and can remove ARGs and ARBs^318–320 but, they fall short in mitigating the dissemination of ARGs and ARB.^{51,91,95,96,235,321–325} These conventional wastewater treatment processes do not specifically target genetic material or residual antimicrobials, allowing ARGs to persist throughout the treatment line.²⁶² Moreover, the conditions within WWTPs – such as dense microbial communities, nutrient-rich environments, and selective pressures from residual antibiotics and disinfectants – can facilitate the horizontal transfer of resistance genes.^41,42 As a result, effluent discharged following conventional treatment, despite meeting established quality standards in high-income settings, may still contain quantifiable levels of antibiotic residues, AMR bacteria, and ARGs.^176,326,327 These are discharged into receiving water bodies, where they contribute to the environmental resistome and may interact with human, animal, and ecological systems.¹¹⁹ Additionally, sewage sludge, which is often applied as agricultural fertilizer, represents another route through which resistance elements re-enter the terrestrial environment.³²⁸ In a comparative metagenomic analysis by Bombaywala et al. (2021), pharmaceutical sediment exhibited the highest proportion of ARGs (2.52%), followed by effluent treatment plant (ETP) sludge (2.28%) and wetland sludge (1.29%).²²² While pharmaceutical discharge sites contained clinically important ARGs such as tetA, tetC, and qnrS, the ETP sludge was enriched with multidrug efflux pump genes, highlighting the persistence of resistance elements even in treated waste streams.^222,329

The growing recognition of wastewater as a major reservoir and conduit for AMR highlights the urgent need to reconsider current treatment paradigms. Addressing AMR in wastewater requires the development of advanced treatment technologies, improved surveillance, and a stronger policy framework that includes resistance as a water quality parameter. Integrating One Health principles into wastewater management is essential to limiting downstream risks and reducing the environmental amplification of resistance.

Septic tanks

Decentralized wastewater systems, such as septic tanks, present distinct challenges in the context of AMR. In rural and suburban areas, an estimated 25% of the population relies on septic systems for domestic wastewater management.³³⁰ These systems function by separating solids and scum from wastewater and allowing the remaining effluent to filter through soil. While they are effective at reducing conventional pollutants, they are not designed to eliminate antibiotics, ARB, or ARGs.³³¹ The presence of these contaminants in effluent can result in their infiltration into nearby groundwater sources, particularly when systems are poorly maintained or located near wells and boreholes.^332,333 Given that human excreta are estimated to contain 10¹¹ bacterial cells per gram of colonic content,³³⁴ it is suggested that the pathogen loads discharged from septic tank effluents may be comparable to, or exceed, infectious dose thresholds capable of initiating disease in exposed populations.³³⁵ Empirical data have shown that faecal coliforms and E. coli in septic effluents can reach concentrations ranging from 10³ to 10⁸ CFU per 100 mL, with survival in groundwater extending up to 30 days for coliforms and over 100 days for E. coli.^321,331 Since private wells are not subject to regulatory oversight and are often used without prior microbiological treatment, they may serve as an unrecognized route of exposure to resistance-related contaminants, such as ARBs and ARGs.³²² Environment and climate change Canada has identified septic systems as a major contributor to groundwater contamination across the country.²³⁵ Supporting this concern, a 2019 study conducted along a 1.7 km stretch of Lake Huron shoreline in Grand Bend, Ontario, detected artificial sweetener acesulfame in 100% groundwater samples (7–842 ng L⁻¹, n = 78), confirming widespread contamination of the surficial aquifer by septic system effluent.³²³ Nitrate levels (3.5 ± 1.4 mg L⁻¹) were positively correlated with acesulfame (r² = 0.54), further implicating septic systems as a substantial source of nitrogen loading to groundwater.³²³ These findings illustrate how septic effluent can influence drinking water quality even when systems are functioning as intended.

Agricultural and veterinary

Antibiotics are widely (mis)used in all areas of industrial agriculture including intensive crop cultivation, livestock farming and aquaculture to treat infections, prevent disease prophylactically, and promote growth.^{2,66,336–338} The rise of AMR in the agricultural sector is detrimental to the health of the crops and animals, causing losses for farmers and consumers. Food animals (terrestrial and aquatic species) farmed as monocrops under crowded and stressful conditions of industrial agricultural practices are prone to infections.^{66,203,338,339} These conditions increase the exposure risks and transport of AMR into other areas such as into the food industry and to the consumer, or into the environment through runoff or leachates.^{70,175,338,340–343} Leachates can also enter environmental waters, into the water table, and further to groundwater.^{70,235,237,338,344} Antibiotics are commonly distributed to livestock in their feed resulting in potential release of antibiotics directly into the local environment risking AMR evolution. The microbiomes within these animals experience constant exposure to antibiotics risking the selective pressure that favours the evolution of AMR. Animals infected with resistant microbes risk spreading AMR between animals and humans through bacteria such as E. coli which are shared pathogens.^66,338,343

Animal livestock

Antibiotic use has been prolific in livestock farming since industrialization. To capitalize on production, factory farming practices, such as maximizing the number of animals in one space, result in sanitary issues and higher rates of disease transmission between animals and ultimately to humans.³³⁹ The World Organization for Animal Health (WOAH) estimated that up to 88 927 tonnes of antimicrobials were consumed for veterinary purposes of food-producing animals (includes aquatic animals) globally in 2021.³⁴⁵ An analysis from Ardakani et al. (2024) determined an estimate of 76 060 tonnes for global annual use of AMAs over 2019–2021 for cattle, chicken, and pigs.³⁴⁶ Analyses show that most antimicrobial agent usage by biomass is in bovine farming. While there are data availability limitations that would affect the calculations methodology, of which were reported by authors, the significant tonnes of antimicrobials (mis)used in livestock agriculture is a high-risk driver of AMR.³⁴⁶

Additionally, the faeces and urine wastes of these farm animals will also contain antibiotics residues, ARGs, and AMR microbes.^{66,71,73,343,347–350} Wastes from livestock can be purposefully used in other agricultural applications such as manure application for fertilization in plant agriculture resulting in another environmental contamination pathway.^{66,70,71,73,95,95,338,343,344,347,348,350} These wastes result in another exposure mechanism pathway into the environment through soil contamination which could percolate to the water table and contaminate agricultural runoff from weather.^{66,70,328,338,344,350} The accumulation overtime of AMR in these soils, particularly in fields where manure is applied annually, results in a persistent AMR contamination and HGT mechanisms allow for the proliferation of AMR among the indigenous environmental bacterial communities.^{70,71,73,95,328,333,347,348,351,352}

Plant agriculture

Plant agriculture is less commonly associated with antibiotics and AMR in agriculture but contribute significantly to environmental AMR through multiple pathways. Plant pathogens including fungi, bacteria and viruses cause infectious disease, such as blight, that can destroy crop yields. Antibiotics like streptomycin and tetracyclines are commonly used in plant agriculture for disease control for their broad-spectrum activity against both gram-negative and gram-positive bacteria. This drives AMR in relevant plant and crop pathogens, such as documented streptomycin resistance in Erwinia amylovora that causes fire blight in apples and pears and Pseudomona syringae that causes cankers, but also in non-pathogenic microbes in the soil and foliage.^{184,204,261,353–355} This inadvertently creates ARG reservoirs in these soil bacteria which can transfer to animal and human pathogens via HGT. Soil contaminations such as metals, pesticides, and other organic pollutants would also exert selective pressures on these microbial communities as discussed previously. All the extracellular AMR DNA persist in the soil environment due to the ability of the DNA to adsorb onto the surfaces of particles, such as clay and sand, that make up soil. DNA/RNA extraction is required for soil samples but the adsorption of DNA to the particulate matter decreases the efficiency of DNA extraction.³⁵⁶

Plant agriculture AMR can be exacerbated by any AMR contamination from manure and/or sludge applications as fertilizers that may contain many AMR microbes and ARGs.^{66,71,338,348,351} Manure and sludge provide a nutrient-rich environment for diverse microbes that can facilitate HGT of ARGs. Unmetabolized antibiotics excreted in livestock waste create sustained selective pressure on soil and plant-associated microbes to develop AMR.^{66,71,73,338,343,347,348,350,351} It has been observed that manure application influences the indigenous soil microbiome more than chemical fertilizer, including increasing the diversity and abundance of ARGs and MGEs. Repeat yearly manure applications provide more nutrients and potentially ARGs to the soil microbiome that would stimulate growth of the soil microbiome including the ARB.^{66,71,328,333,338,344,347,348,350,351} A unique factor of sludge being a product of human sewage waste results in introducing clinically relevant microbes or ARGs to the soil microbial community.^{95,158,328,352} A study on postdigestion sewage sludge in Ontario found that the AMA triclosan was the predominant contaminant applying selective pressure to microbes among 100+ other compounds.¹⁵⁸ Although further treatment of these waste products may reduce AMR microbe or ARG presence, providers often fail to adequately treat them prior to transport and field application, risking worker and environmental exposure.^{73,166,328,329,357} Farmed crops ultimately serve as pathways for AMR and resistant microbes into the food chain.

Aquaculture

Aquaculture is a growing agricultural practice as global fishery yields decrease with increasing demand for aquatic proteins and delicacies.³³⁷ According to the Food and Agriculture Organization (FAO) of the United Nations, aquaculture produced 51% of global aquatic animal production in 2022, representing 94.4 million tonnes, surpassing fisheries production for the first time.³³⁷ This presents a significant source and transport mechanism for AMR because of the nature of aquaculture and the ability to enter the food chain. Aquaculture systems are unique in that the production infrastructure is directly within and surrounded by wild aquatic ecosystems. Being inside of the larger aquatic or marine environment, there is an additional consideration for the ubiquitous presence of microplastics and metals in the water. As previously described, microplastics offer high surface area substrates for biofilms to develop on and bioaccumulate in the food chain via ingestion by the aquatic animals.^{137,140,145–147,337}

Aquaculture uses the same principles of maximizing the number of animals as monocrops within a given space. These crowded and stressful environments give rise to vulnerability to disease.^{58,146,337,339,341} Like livestock agriculture, the antibiotics are provided through the animal feed which must be put directly into the aquaculture open nets that are in the lake or ocean ecosystem. This results in a direct addition of antimicrobials and AMR to the environment and the wild aquatic life. Interactions of the AMR microbes in the wild environment, such as HGT or farmed and/or wild animal infection, facilitates the evolution, spread, and exposure of AMR across many environments over large geographical areas.^{58,146,337,339,341} There are also climate considerations as warmer water temperatures can facilitate pathogen growth and disease outbreak in the aquatic animal and/or algae monocrops.^223,224,337

Waste and landfills

Pollutants from landfills can often be released into the local environment, such as via leachates, resulting in landfills being a potential long-term source of AMR and particularly ARGs.^72,87,209 Landfills receive hundreds of millions of tonnes of solid waste containing food and other organic pollutants, metals, plastics, personal care products, and pharmaceuticals, including antibiotics.^209,216,358 These wastes and pollutants collectively exert selective pressures on the microbial communities over decades. Depending on the composition of wastes and environmental conditions (water, nutrients, oxygen, pH, etc), effectively creating microenvironments, the spatio-temporal distribution and proliferation of AMR and ARGs could vary dramatically.^{61,87–89,209,216,225,260,359} This results in very heterogenous samples and microbial communities that are dependent on consumerism and other local anthropogenic activities. For example, Threedeach et al. (2012) tested the susceptibility of 80 E. coli isolates from leachates of anaerobic and semi-aerobic landfill systems to 31 antibiotics using the Kirby–Bauer disk diffusion test. Their results found that over 80% of E. coli from both leachate types were found to be resistant to one or more antibiotics and that generally the isolates from the anaerobic leachates had higher percentage of resistances.⁸⁹

Extensive landfill studies have been completed in China including in the context of AMR. Song et al. (2016) found that sulfamethoxazole, tetracycline, and oxytetracycline antibiotics were abundant in refuse samples obtained from different areas and depths in a large landfill in central China. The related ARGs (sul1 and tetO) were detected, with large variation, in all their 51 refuse samples using qPCR.³⁶⁰ These genes were positively correlated with sample moisture content of which Song et al. suggest that ARGs may be proliferated or transported with water in landfills. Wu et al. (2017) conducted a comprehensive study of a landfill in Shanghai, China with various leachate and refuse samples taken at different ages of the landfill.⁸⁷ The study quantified four classes of antibiotics (tetracyclines, sulfonamides, macrolides, and β-lactams) with UPLC-MS/MS, metals (Al, As, Fe, Ni, Cr, Cu, Mn, Pb, Zn, Cd, and Co) with ICP-OES, 12 ARGs and 6 mobile genetic elements (MGEs) via qPCR, as well as other physicochemical properties such as pH and content of N and P. Wu et al. (2017) found that all metals tested, except for Co, were found in all their leachate and refuse samples. Their study revealed trends where antibiotics concentrations decreased, metals concentrations increased, and concentrations of ARGs and MGEs increased with landfill age in leachates.⁸⁷ The positive correlation of increasing metals concentrations and increasing ARGs in leachates with landfill age suggests a co-selective pressure on the landfill microbial communities^{87,88,225,226,359} of which Wu et al. (2017) determined their samples were composed of at least 23 phyla.⁸⁷ A study by Wang et al. (2022) used qPCR and Illumina sequencing to analyse 56 leachate samples collected from seven representative landfills in China. It was determined that 1210 known ARGs were identified, including several multi-drug ARGs such as EmrB-QacA, mdtE, mdtL that encode multidrug transporters and efflux complexes. Alarmingly, pathogenic species Salmonella enterica, Labilibaculum filiforme, Bacteroidales bacterium, Anaeromassilibacillus senegalensis, and Pseudochrobactrum sp. were identified as ARG carriers.⁸⁸ There may also be missing information in their data given that the leachates that were centrifuged and the DNA extractions were performed on the pellet only with a soil extraction kit while the supernatant was discarded.

Several landfill studies show that concentrations of ARGs increased with landfill age^{87,216,260,360,361} while others show a negative correlation.²²⁵ The innate variability in landfill samples due to localized anthropogenic behaviours, waste types, and biogeochemical differences make it difficult to determine trends in the abundance, fate and transport of AMR and ARGs in landfills without large-scale international co-operative spatio-temporal studies. For example, the differences in abundance of ARGs in leachates and refuse with plastics is that there are a high presence and continuous production of microplastics in landfills, and any attached AMR biofilms, that could be in samples.^{51,123,135,278} It has also been shown that bacteria may selectively colonize some substrates like microplastics more than wood or glass with factors like surface area and surface charge affecting selectivity.^63,138 The quality of landfill refuse samples could vary given that analytes must be extracted from portions of heterogenous solid wastes at various stages of decomposition which may first need to be reduced in size via homogenization. The composition of refuse samples then must be characterized to interpret results appropriately and multiple samples should be taken from various locations and depths in the landfills.

Another transport mechanism that can complicate the issue of AMR in landfills is through bioaerosols released from landfill sites due to various waste disposal activities and local environmental conditions.^205,208,209 For example, a worker could be transporting and unloading a truck full of waste to a landfill site and releasing small particulate matter, dust, and bioaerosols into the air. If there is not appropriate personal protective equipment for the worker, bioaerosols can enter their respiratory tract. Due to the heterogeneous and complex microbial community in different parts of landfills, the bioaerosols are thus also quite complex and could include microbial toxins, viruses, dust, ARGs, and possibly cells or parts of colonies that are attached to the small particulate matter. The released bioaerosols can result in health impacts for the landfill employees and any nearby communities of microbes, animals, and humans.

While modern landfill sites manage the collection and decontamination of leachates, ARGs and AMR microbes have been shown to persist in treated leachates. Thus, there are still risks of leachates entering the local environment, surface waters, and potentially into the water table via infiltration.^{86,88,89,148,148,209,225,226,232,260,362} A recent study by Jia et al. (2024) used metagenomic sequencing to determine the antibiotic resistome of raw and treated leachates and in the groundwater surrounding three landfills where ARGs were found in all samples. Human pathogens Acinetobacter pittii, Pseudomonas stutzeri, and Pseudomonas alcaligenes were found to be carriers in the leachate and groundwater samples.²³²

Groundwater

Groundwater is a key source of drinking water in many regions, particularly in rural and urban areas. In the US and Canada, groundwater serves approximately 43 million and 3–4 million people, respectively.^322,363 While typically perceived as a protected resource, growing evidence suggests that groundwater can act as a reservoir for antibiotics, ARBs, and ARGs.^19,237 These contaminants may enter aquifers through several pathways, including leaching from manure-amended soils, infiltration from poorly maintained septic systems, and surface runoff from agricultural and wastewater impacted sites.^70,364 A study in the Republic of Ireland looking at private wells found that all E. coli isolates displayed resistance to at least one veterinary antibiotic, with 93% resistant to aminoglycosides, while 21.4% were resistant to human-use antibiotics.³⁶⁵ Similarly, a Canadian cross-sectional study of 878 individuals reported that those consuming groundwater contaminated with AMR E. coli were 26% more likely to be colonized by resistant strains, with 41% carrying resistant E. coli and 28% carrying multidrug-resistant isolates.¹²¹ Once introduced, antibiotics and resistance elements may persist in subsurface environments due to limited degradation processes and relatively stable physicochemical conditions. Microbial communities in groundwater systems can further facilitate the survival or horizontal transfer of ARGs, contributing to the environmental resistome. Areas near septic tanks or intensive agricultural activity are vulnerable, especially where private wells are used without regulatory oversight or microbiological treatment.^{233,237,366,367}

The persistence of AMR-related contaminants in groundwater presents a direct exposure risk to humans and animals.⁶⁶ Furthermore, the remediation of contaminated aquifers is technically challenging and often cost-prohibitive, highlighting the importance of upstream controls. Recognizing groundwater not only as a receptor but also as a potential long-term reservoir for AMR highlights the need to further investigate its role within broader environmental transmission pathways.

Across these environments, AMR does not arise from isolated sources but from a web linking clinical practice, community antibiotic use, waste management, agriculture, and environmental reservoirs. A systems-thinking, One Health lens therefore requires analytical designs that treat hospitals, wastewater and septic systems, agricultural and aquaculture operations, landfills, and groundwater not as separate case studies, but as interconnected nodes within a shared AMR network, where signals can be traced along pathways. By aligning sampling strategies, locations, co-contaminant measurements, and data integration across these sectors, AMR surveillance can move beyond compartmentalized monitoring and toward an integrated analytical system capable of capturing emergent resistance dynamics to inform coordinated, cross-sector interventions.

Conclusions, synthesis, and recommendations towards an integrated One Health approach

This review presents the current progress in AMR detection technologies and AMR research that inform response initiatives such as monitoring programs. This review also presents many of the significant knowledge gaps and challenges in the global approach to addressing AMR. The multidisciplinary, multisectoral, complex, interconnected issue of AMR requires a response that is just as multifaceted in its international cooperation. However, the current research, monitoring, and policy remain in separate sectors and samples are analysed without analysing other co-contaminants or consider how co-contaminants effect AMR analysis despite the widespread recognition of the interconnected nature of AMR. This disconnected approach does not reflect real-world samples and the reality of the complex and dynamic AMR problem, reflecting a limitation to effective AMR mitigation.

Main limitations of the existing system

Compartmentalization of AMR issues has resulted in differences in methodology and language which prevents meaningful comparisons across sectors ultimately creating inconsistent messaging to all stakeholders. The consequences extend beyond academic inefficiency. When AMR surveillance systems operate independently across human, animal, and environmental health sectors, we lose important insights about AMR transport and transmission pathways. For instance, the role of environmental reservoirs like landfill leachate, wild animals, and WWTPs in AMR dissemination and transmission is poorly understood and is not factored into broader resistance monitoring frameworks. To address the knowledge gaps in understanding AMR and any methodological inconsistencies comprehensively across sectors, stakeholders, countries, etc, mass data collection and data sharing must be a priority. Many of the technologies described in Table 2 showed capability to achieve results within hours instead of days and potential for portable systems, but these methods are not yet ready for reliable, rigorous, real-time monitoring. Thus, there is an urgent demand for innovative detection technologies and infrastructure to collect real-time data to monitor and reveal spatiotemporal trends across different environments and sample types.

Specific recommendations for analytical decisions and design

The AMR-interacting co-contaminants and key environmental case studies discussed in this review highlight that AMR emergence and dissemination are shaped by interconnected sources, pathways, and co-contaminants across human, animal, and environmental systems. To translate this systems-understanding into practice, analytical scientists need concrete design principles for sampling, measurement, and data integration. The following points summarize specific analytical considerations that can guide the development of AMR monitoring systems capable of capturing these complex dynamics and support more effective interventions:

• Multi-matrix compatibility. Designing extraction methodologies and/or new sensing technologies to analyse AMR across various sample matrices such as environmental waters, wastewater, soil, mixed-materials refuse, etc. This would also address heterogenous sample matrices.

• Multi-analyte considerations to account for co-contaminant and co-selection of AMR in key environments. For example, monitoring methodology in environments such as animal agriculture should consider measurements of veterinary antibiotics, metals, and pesticides in water, soil, and manure samples to interpret AMR data in context.

• Sampling across key interconnected environments. Design AMR monitoring networks to include key locations where different environmental sectors intersect to allow for transport pathway analysis. Including the location, sampling depth, and sampling time metadata could help with pooled spatiotemporal analysis.

• Design for data comparability and integration with standardized cross-sectoral AMR indicator targets and units reporting. Determine routine genotypic and phenotypic data that must be reported across human, animal, and environmental surveillance to allow for quantitative comparisons and integrated datasets.

Suggestions for the future at a global structural context

The threat of AMR and its ease in transport must drive investment in research and development of new detection technologies, but also remove policy, detection, and treatment implementation barriers for all peoples internationally, particularly in low- and middle-income communities. The WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) has expanded from 25 participating countries in 2016 to 104 countries in 2023. However, 48% of countries did not report data to GLASS in 2023, and approximately half of reporting countries still lack the system infrastructure to generate reliable data.³⁶⁸ Despite recognition of AMR as a global health priority, there is a discrepancy between statements of priority and research funding investments. A critical barrier exists between research and policy implementation as a vicious cycle: insufficient research funding leads to incomplete understanding of AMR dynamics, which in turn provides policymakers with justification for inaction. AMR research requires long-term investments to generate meaningful results, and the complex global nature of AMR requires coordinated international research and responses.^{16,103,368,369}

This level of cross-sectoral, international cooperation in research, knowledge sharing, and implementing responses would require a restructuring of politics and our current segregated competitive systems and funding agencies that results in transnational collaboration, global solidarity, and collective care. This can look like building the research and operations infrastructure and developing a standardized international AMR stewardship program that educates operators and the public for successful autonomous operations, particularly in low-resourced areas to set up everyone for successful AMR responses. It could look like ensuring treatment for infections are accessible as well as developing and educating about new vaccines to prevent infection.

The high costs of AMR in healthcare, manufacturing antibiotics, agricultural and economic productivity, and human and animal lives are known.^{15,16,97,236,368,369} Decreasing the prevalence and impact of AMR would reduce those costs, where monetary savings could be redistributed to other government departments that directly benefit the people. However, the response to AMR must not be driven by money or profit where new technologies are inaccessible or render AMR monitoring and stewardship programs inaccessible. Rather, it must be driven by our social responsibility to uphold fundamental human rights, and in particular, human rights to clean water, well-being, medical care, education, and the right to share in scientific advancement and its benefits, as outlined in articles 26–29 in the Universal Declaration of Human Rights by the United Nations.³⁷⁰ AMR must also be considered when working to address the United Nation's SDGs. While AMR is relevant to all SDGs, the importance of water to human and environmental life and health and consequently has implications on global economic productivity and social inequality, AMR is directly linked to and can be addressed specifically in the following SDGs:¹⁵

(1) No poverty – an additional 28.3 million people could be pushed into extreme poverty by 2050 due to chronic AMR infections and increasing treatment costs making treatment for the poor inaccessible.

(2) Good health and wellbeing – chronic infections and deaths by AMR will increase.

(3) Clean water and sanitation – lack of access facilitates spread of disease and increases morbidity and mortality while the environment has also become a reservoir for AMR.

(4) Reduced inequalities – chronic infections and increasing treatment costs will increase inequalities in already vulnerable groups such as racial and gender minorities, children, people employed in sectors like healthcare or agriculture, and people living in poverty

(5) Responsible consumption and production – misuse of antibiotics in agricultural and clinical contexts and antimicrobial compounds released from wastewaters of pharmaceutical companies and healthcare sites pollute the environment and promote AMR, food safety and security is increasingly threatened by AMR.

(6) Climate Action – global warming has resulted in increased frequency and severity of extreme weather events which along with forced migration spread AMR.

(7) Life Below Water – aquatic environments have become reservoirs for AMR and misuse of antimicrobials in aquaculture promote AMR.

(8) Life on Land – antimicrobial pollution and AMR spread, and reservoirs such as soils and animals change ecosystems and decrease biodiversity.

(9) Partnerships for the goals – transnational, interdisciplinary, cross-sectoral, collaborative partnerships and research and development is required to effectively address AMR.

AMR within this SDG and human-rights context reinforces the systems thinking and One Health lenses in that analytical systems, surveillance strategies, and technological investments must be designed not only for analytical performance, but also for accessibility and equity to ensure that the science is for the people. AMR research has previously conducted within sectoral silos, and it is important that this research continues to obtain more data and deeper understandings of AMR in different contexts. However, the underestimated complex role of the environment and vast number of interconnected transport pathways between and within sectors call for urgent expansion into interdisciplinary transnational collaborative research and systems thinking guided by the One Health Framework. Only a coordinated global response, informed by the inclusion of Indigenous knowledge keepers and scientists, will result in equitable comprehensive meaningful change in addressing the worsening AMR crisis and protect present and future generations for all.

Author contributions

Rebecca X. Y. Chen: writing – review & editing, writing – original draft, visualization, conceptualization. Rayane Azani: writing – review & editing, writing – original draft, visualization. Kayla Elliot: writing – review & editing, writing – original draft. Sarah Jane Payne: writing – review & editing, writing – original draft, conceptualization. R. Stephen Brown: writing – review & editing, writing – original draft, supervision, conceptualization. Zhe She: writing – review & editing, writing – original draft, supervision, conceptualization.

Conflicts of interest

All authors confirm there are no conflicts to declare.

Data availability

No primary research results and original or new experimental data was generated or analysed as part of this review article.

Acknowledgements

The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Program and the Ontario Research Fund (ORF) Research Excellence program for financial supports. TOC graphic was created in BioRender. She, Z. (2025). Fig. 1 was created by Azani, R.

References

1. D. G. J. Larsson, W. H. Gaze, R. Laxminarayan and E. Topp, AMR, One Health and the Environment, Nat. Microbiol., 2023, 8(5), 754–755,  DOI:10.1038/s41564-023-01351-9.
2. M. E. Velazquez-Meza, M. Galarde-López, B. Carrillo-Quiróz and C. M. Alpuche-Aranda, Antimicrobial Resistance: One Health Approach, Vet. World, 2022, 743–749,  DOI:10.14202/vetworld.2022.743-749.
3. A. White and J. M. Hughes, Critical Importance of a One Health Approach to Antimicrobial Resistance, EcoHealth, 2019, 16(3), 404–409,  DOI:10.1007/s10393-019-01415-5.
4. A. Fleming, On the Antibacterial Action of Cultures of a Penicillium, with Special Reference to Their Use in the Isolation of B, Influenzae, Br. J. Exp. Pathol., 1929, 10, 226–236.
5. W. M. M. Kirby, Extraction of a Highly Potent Penicillin Inactivator from Penicillin Resistant Staphylococci, Science, 1944, 99(2579), 452–453.
6. S. Ross, W. Rodriguez, G. Controni and W. Khan, Staphylococcal Susceptibility to Penicillin G: The Changing Pattern Among Community Strains, J. Am. Med. Assoc., 1974, 229(8), 1075–1077,  DOI:10.1001/jama.1974.03230460025014.
7. F. Baquero, J.-L. Martínez and R. Cantón, Antibiotics and Antibiotic Resistance in Water Environments, Curr. Opin. Biotechnol., 2008, 19(3), 260–265,  DOI:10.1016/j.copbio.2008.05.006.
8. C. Llor and L. Bjerrum, Antimicrobial Resistance: Risk Associated with Antibiotic Overuse and Initiatives to Reduce the Problem, Ther. Adv. Drug Saf., 2014, 5(6), 229–241,  DOI:10.1177/2042098614554919.
9. B. B. Finlay, J. Conly, P. C. Coyte, J. R. Dillon, G. Douglas, E. Goddard, L. Greco, L. E. Nicolle, D. Patrick, J. F. Prescott, A. Quesnel-Vallée, R. Smith and G. Wright, “When Antibiotics Fail”, in The Expert Panel on the Potential Socio-Economic Impacts of Antimicrobial Resistance in Canada, Council of Canadian Academies, Ottawa, ON, CA, 2019.
10. J. M. Munita and C. A. Arias, Mechanisms of Antibiotic Resistance, Microbiol. Spectrum, 2016, 4(2) DOI:10.1128/microbiolspec.VMBF-0016-2015.
11. J. M. A. Blair, M. A. Webber, A. J. Baylay, D. O. Ogbolu and L. J. V. Piddock, Molecular Mechanisms of Antibiotic Resistance, Nat. Rev. Microbiol., 2015, 13(1), 42–51,  DOI:10.1038/nrmicro3380.
12. O. B. Jonas, A. Irwin, F. C. J. Berthe, F. G. Le Gall and P. V. Marquez, Drug-Resistant Infections: A Threat to Our Economic Future (Vol. 2 of 2), Final Report (English), 114679, World Bank Group, Washington, DC, 2017, https://documents.worldbank.org/curated/en/323311493396993758.
13. C. J. L. Murray, K. S. Ikuta, F. Sharara, L. Swetschinski, G. Robles Aguilar, A. Gray, C. Han, C. Bisignano, P. Rao, E. Wool, S. C. Johnson, A. J. Browne, M. G. Chipeta, F. Fell, S. Hackett, G. Haines-Woodhouse, B. H. Kashef Hamadani, E. A. P. Kumaran, B. McManigal, S. Achalapong, R. Agarwal, S. Akech, S. Albertson, J. Amuasi, J. Andrews, A. Aravkin, E. Ashley, F.-X. Babin, F. Bailey, S. Baker, B. Basnyat, A. Bekker, R. Bender, J. A. Berkley, A. Bethou, J. Bielicki, S. Boonkasidecha, J. Bukosia, C. Carvalheiro, C. Castañeda-Orjuela, V. Chansamouth, S. Chaurasia, S. Chiurchiù, F. Chowdhury, R. Clotaire Donatien, A. J. Cook, B. Cooper, T. R. Cressey, E. Criollo-Mora, M. Cunningham, S. Darboe, N. P. J. Day, M. De Luca, K. Dokova, A. Dramowski, S. J. Dunachie, T. Duong Bich, T. Eckmanns, D. Eibach, A. Emami, N. Feasey, N. Fisher-Pearson, K. Forrest, C. Garcia, D. Garrett, P. Gastmeier, A. Z. Giref, R. C. Greer, V. Gupta, S. Haller, A. Haselbeck, S. I. Hay, M. Holm, S. Hopkins, Y. Hsia, K. C. Iregbu, J. Jacobs, D. Jarovsky, F. Javanmardi, A. W. J. Jenney, M. Khorana, S. Khusuwan, N. Kissoon, E. Kobeissi, T. Kostyanev, F. Krapp, R. Krumkamp, A. Kumar, H. H. Kyu, C. Lim, K. Lim, D. Limmathurotsakul, M. J. Loftus, M. Lunn, J. Ma, A. Manoharan, F. Marks, J. May, M. Mayxay, N. Mturi, T. Munera-Huertas, P. Musicha, L. A. Musila, M. M. Mussi-Pinhata, R. N. Naidu, T. Nakamura, R. Nanavati, S. Nangia, P. Newton, C. Ngoun, A. Novotney, D. Nwakanma, C. W. Obiero, T. J. Ochoa, A. Olivas-Martinez, P. Olliaro, E. Ooko, E. Ortiz-Brizuela, P. Ounchanum, G. D. Pak, J. L. Paredes, A. Y. Peleg, C. Perrone, T. Phe, K. Phommasone, N. Plakkal, A. Ponce-de-Leon, M. Raad, T. Ramdin, S. Rattanavong, A. Riddell, T. Roberts, J. V. Robotham, A. Roca, V. D. Rosenthal, K. E. Rudd, N. Russell, H. S. Sader, W. Saengchan, J. Schnall, J. A. G. Scott, S. Seekaew, M. Sharland, M. Shivamallappa, J. Sifuentes-Osornio, A. J. Simpson, N. Steenkeste, A. J. Stewardson, T. Stoeva, N. Tasak, A. Thaiprakong, G. Thwaites, C. Tigoi, C. Turner, P. Turner, H. R. Van Doorn, S. Velaphi, A. Vongpradith, M. Vongsouvath, H. Vu, T. Walsh, J. L. Walson, S. Waner, T. Wangrangsimakul, P. Wannapinij, T. Wozniak, T. E. M. W. Young Sharma, K. C. Yu, P. Zheng, B. Sartorius, A. D. Lopez, A. Stergachis, C. Moore, C. Dolecek and M. Naghavi, Global Burden of Bacterial Antimicrobial Resistance in 2019: A Systematic Analysis, Lancet, 2022, 399(10325), 629–655,  DOI:10.1016/S0140-6736(21)02724-0.
14. Centers for Disease Control and Prevention (U.S.), Antibiotic Resistance Threats in the United States, 2019, Centers for Disease Control and Prevention (U.S.), 2019.  DOI:10.15620/cdc:82532.
15. World Health Organization (WHO), Food and Agriculture Organization (FAO), World Organisation for Animal Health (OIE), United Nations Environment Programme (UNEP), Antimicrobial Resistance and the United Nations Sustainable Development Cooperation Framework: Guidance for United Nations Country Teams, World Health Organization, Geneva, 2021.
16. United Nations Environment Programme (UNEP), Bracing for Superbugs: Strengthening Environmental Action in the One Health Response to Antimicrobial Resistance, United Nations Environment Programme (UNEP), Nairobi, Kenya, 2023.
17. S. Singh, A. Numan and S. Cinti, Point-of-Care for Evaluating Antimicrobial Resistance through the Adoption of Functional Materials, Anal. Chem., 2022, 94(1), 26–40,  DOI:10.1021/acs.analchem.1c03856.
18. Z. Alfahl, A. Chueiri, S. Carolan, G. Darcy, N. Hussain, N. Cahill and L. O'Connor, Antimicrobial Resistance Detection Methods in Water Environments: A Scoping Review, Sustain. Microbiol., 2024, 1(1), qvae034,  DOI:10.1093/sumbio/qvae034.
19. A. Elbehiry, E. Marzouk, A. Abalkhail, M. H. Abdelsalam, M. E. A. Mostafa, M. Alasiri, M. Ibrahem, A. T. Ellethy, A. Almuzaini, S. N. Aljarallah, A. Abu-Okail, N. Marzook, S. Alhadyan and H. M. Edrees, Detection of Antimicrobial Resistance via State-of-the-Art Technologies versus Conventional Methods, Front. Microbiol., 2025, 16, 1549044,  DOI:10.3389/fmicb.2025.1549044.
20. M. F. Anjum, E. Zankari and H. Hasman, Molecular Methods for Detection of Antimicrobial Resistance, Microbiol. Spectrum, 2017, 5(6) DOI:10.1128/microbiolspec.ARBA-0011-2017.
21. M. Oliveira, W. Antunes, S. Mota, Á Madureira-Carvalho, R. J. Dinis-Oliveira and D. D. da Silva, An Overview of the Recent Advances in Antimicrobial Resistance, Microorganisms, 2024, 12(9), 1920,  DOI:10.3390/microorganisms12091920.
22. D. G. J. Larsson and C.-F. Flach, Antibiotic Resistance in the Environment, Nat. Rev. Microbiol., 2022, 20(5), 257–269,  DOI:10.1038/s41579-021-00649-x.
23. S. A. Kraemer, A. Ramachandran and G. G. Perron, Antibiotic Pollution in the Environment: From Microbial Ecology to Public Policy, Microorganisms, 2019, 7(6), 180,  DOI:10.3390/microorganisms7060180.
24. K. E. Arnold, G. Laing, B. J. McMahon, S. Fanning, D. J. Stekel, O. Pahl, L. Coyne, S. M. Latham and K. M. McIntyre, The Need for One Health Systems-Thinking Approaches to Understand Multiscale Dissemination of Antimicrobial Resistance, Lancet Planet. Health, 2024, 8(2), e124–e133,  DOI:10.1016/S2542-5196(23)00278-4.
25. Systems Thinking for Health Systems Strengthening, ed. D. De Savigny, T. Adam, Alliance for Health Policy and Systems Research and World Health Organization, Alliance for Health Policy and Systems Research, World Health Organization, Geneva, 2009.
26. R. Kock, K. Queenan, J. Garnier, R. L. Nielsen, S. Buttigieg, D. De Meneghi, M. Holmberg, J. Zinsstag, S. Rüegg and B. Häsler, Health Solutions: Theoretical Foundations of the Shift from Sectoral to Integrated Systems, in Integrated approaches to health, ed. S. R. Rüegg, B. Häsler and J. Zinsstag, Brill | Wageningen Academic, 2018, pp. 22–37.  DOI:10.3920/9789086868759_004.
27. D. A. Luke and K. A. Stamatakis, Systems Science Methods in Public Health: Dynamics, Networks, and Agents, Annu. Rev. Public Health, 2012, 33(1), 357–376,  DOI:10.1146/annurev-publhealth-031210-101222.
28. J. Momsen, E. B. Speth, S. Wyse and T. Long, Using Systems and Systems Thinking to Unify Biology Education, CBE: Life Sci. Educ., 2022, 21(2), es3,  DOI:10.1187/cbe.21-05-0118.
29. M. A. Salam, M. Y. Al-Amin, M. T. Salam, J. S. Pawar, N. Akhter, A. A. Rabaan and M. A. A. Alqumber, Antimicrobial Resistance: A Growing Serious Threat for Global Public Health, Healthcare, 2023, 11(13), 1946,  DOI:10.3390/healthcare11131946.
30. O. X. Cordero, H. Wildschutte, B. Kirkup, S. Proehl, L. Ngo, F. Hussain, F. Le Roux, T. Mincer and M. F. Polz, Ecological Populations of Bacteria Act as Socially Cohesive Units of Antibiotic Production and Resistance, Science, 2012, 337(6099), 1228–1231,  DOI:10.1126/science.1219385.
31. G. Muteeb, M. T. Rehman, M. Shahwan and M. Aatif, Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review, Pharmaceuticals, 2023, 16(11), 1615,  DOI:10.3390/ph16111615.
32. S.-J. Li, Z.-S. Hua, L.-N. Huang, J. Li, S.-H. Shi, L.-X. Chen, J.-L. Kuang, J. Liu, M. Hu and W.-S. Shu, Microbial Communities Evolve Faster in Extreme Environments, Sci. Rep., 2014, 4(1), 6205,  DOI:10.1038/srep06205.
33. F. A. Hussain, J. Dubert, J. Elsherbini, M. Murphy, D. VanInsberghe, P. Arevalo, K. Kauffman, B. K. Rodino-Janeiro, H. Gavin, A. Gomez, A. Lopatina, F. Le Roux and M. F. Polz, Rapid Evolutionary Turnover of Mobile Genetic Elements Drives Bacterial Resistance to Phages, Science, 2021, 374(6566), 488–492,  DOI:10.1126/science.abb1083.
34. H. Nikaido, Prevention of Drug Access to Bacterial Targets: Permeability Barriers and Active Efflux, Science, 1994, 264(5157), 382–388,  DOI:10.1126/science.8153625.
35. S. I. Miller, Antibiotic Resistance and Regulation of the Gram-Negative Bacterial Outer Membrane Barrier by Host Innate Immune Molecules, mBio, 2016, 7(5), e01541–16,  DOI:10.1128/mBio.01541-16.
36. M. I. Hutchings, A. W. Truman and B. Wilkinson, Antibiotics: Past, Present and Future, Curr. Opin. Microbiol., 2019, 51, 72–80,  DOI:10.1016/j.mib.2019.10.008.
37. N. A. Lerminiaux and A. D. S. Cameron, Horizontal Transfer of Antibiotic Resistance Genes in Clinical Environments, Can. J. Microbiol., 2019, 65(1), 34–44,  DOI:10.1139/cjm-2018-0275.
38. C. Pal, J. Bengtsson-Palme, E. Kristiansson and D. G. J. Larsson, Co-Occurrence of Resistance Genes to Antibiotics, Biocides and Metals Reveals Novel Insights into Their Co-Selection Potential, BMC Genomics, 2015, 16(1), 964,  DOI:10.1186/s12864-015-2153-5.
39. A. Di Cesare, E. Eckert and G. Corno, Co-Selection of Antibiotic and Heavy Metal Resistance in Freshwater Bacteria, J. Limnol., 2016, 75(s2), 59–66,  DOI:10.4081/jlimnol.2016.1198.
40. D. A. Fitzpatrick, Horizontal Gene Transfer in Fungi, FEMS Microbiol. Lett., 2012, 329(1), 1–8,  DOI:10.1111/j.1574-6968.2011.02465.x.
41. J. Ma, H. Sun, B. Li, B. Wu, X. Zhang and L. Ye, Horizontal Transfer Potential of Antibiotic Resistance Genes in Wastewater Treatment Plants Unraveled by Microfluidic-Based Mini-Metagenomics, J. Hazard. Mater., 2024, 465, 133493,  DOI:10.1016/j.jhazmat.2024.133493.
42. C. L. Brown, A. Maile-Moskowitz, A. J. Lopatkin, K. Xia, L. K. Logan, B. C. Davis, L. Zhang, P. J. Vikesland and A. Pruden, Selection and Horizontal Gene Transfer Underlie Microdiversity-Level Heterogeneity in Resistance Gene Fate during Wastewater Treatment, Nat. Commun., 2024, 15(1), 5412,  DOI:10.1038/s41467-024-49742-8.
43. V. J. Savage, I. Chopra and A. J. O'Neill, Staphylococcus Aureus Biofilms Promote Horizontal Transfer of Antibiotic Resistance, Antimicrob. Agents Chemother., 2013, 57(4), 1968–1970,  DOI:10.1128/AAC.02008-12.
44. J. S. Madsen, M. Burmølle, L. H. Hansen and S. J. Sørensen, The Interconnection between Biofilm Formation and Horizontal Gene Transfer, FEMS Immunol. Med. Microbiol., 2012, 65(2), 183–195,  DOI:10.1111/j.1574-695X.2012.00960.x.
45. P. N. A. Harris and J. K. Ferguson, Antibiotic Therapy for Inducible AmpC β-Lactamase-Producing Gram-Negative Bacilli: What Are the Alternatives to Carbapenems, Quinolones and Aminoglycosides?, Int. J. Antimicrob. Agents, 2012, 40(4), 297–305,  DOI:10.1016/j.ijantimicag.2012.06.004.
46. D. M. Prigozhin, I. V. Krieger, J. P. Huizar, D. Mavrici, G. S. Waldo, L.-W. Hung, J. C. Sacchettini, T. C. Terwilliger and T. Alber, Subfamily-Specific Adaptations in the Structures of Two Penicillin-Binding Proteins from Mycobacterium Tuberculosis, PLoS One, 2014, 9(12), e116249,  DOI:10.1371/journal.pone.0116249.
47. C. C. S. Fuda, J. F. Fisher and S. Mobashery, β-Lactam Resistance in Staphylococcus Aureus: The Adaptive Resistance of a Plastic Genome, Cell. Mol. Life Sci., 2005, 62(22), 2617–2633,  DOI:10.1007/s00018-005-5148-6.
48. M. T. Mata, F. Baquero and J. C. Pérez-Díaz, A Multidrug Efflux Transporter in Listeria Monocytogenes, FEMS Microbiol. Lett., 2000, 187(2), 185–188,  DOI:10.1111/j.1574-6968.2000.tb09158.x.
49. P. T. LaBreck, A. C. Bochi-Layec, J. Stanbro, G. Dabbah-Krancher, M. P. Simons and D. S. Merrell, Systematic Analysis of Efflux Pump-Mediated Antiseptic Resistance in Staphylococcus Aureus Suggests a Need for Greater Antiseptic Stewardship, mSphere, 2020, 5(1), e00959–19,  DOI:10.1128/mSphere.00959-19.
50. Y. Wang, J. Lu, L. Mao, J. Li, Z. Yuan, P. L. Bond and J. Guo, Antiepileptic Drug Carbamazepine Promotes Horizontal Transfer of Plasmid-Borne Multi-Antibiotic Resistance Genes within and across Bacterial Genera, ISME J., 2019, 13(2), 509–522,  DOI:10.1038/s41396-018-0275-x.
51. S. Jacquiod, A. Brejnrod, S. M. Morberg, W. Abu Al-Soud, S. J. Sørensen and L. Riber, Deciphering Conjugative Plasmid Permissiveness in Wastewater Microbiomes, Mol. Ecol., 2017, 26(13), 3556–3571,  DOI:10.1111/mec.14138.
52. A. Monier, A. Pagarete, C. De Vargas, M. J. Allen, B. Read, J.-M. Claverie and H. Ogata, Horizontal Gene Transfer of an Entire Metabolic Pathway between a Eukaryotic Alga and Its DNA Virus, Genome Res., 2009, 19(8), 1441–1449,  DOI:10.1101/gr.091686.109.
53. S. Goh, H. Hussain, B. J. Chang, W. Emmett, T. V. Riley and P. Mullany, Phage ϕC2 Mediates Transduction of Tn 6215, Encoding Erythromycin Resistance, between Clostridium Difficile Strains, mBio, 2013, 4(6), e00840-13,  DOI:10.1128/mBio.00840-13.
54. Y. Wang, J. Lu, J. Engelstädter, S. Zhang, P. Ding, L. Mao, Z. Yuan, P. L. Bond and J. Guo, Non-Antibiotic Pharmaceuticals Enhance the Transmission of Exogenous Antibiotic Resistance Genes through Bacterial Transformation, ISME J., 2020, 14(8), 2179–2196,  DOI:10.1038/s41396-020-0679-2.
55. L. V. Mallet, J. Becq and P. Deschavanne, Whole Genome Evaluation of Horizontal Transfers in the Pathogenic Fungus Aspergillus Fumigatus, BMC Genomics, 2010, 11(1), 171,  DOI:10.1186/1471-2164-11-171.
56. T. Anand, B. C. Bera, R. K. Vaid, S. Barua, T. Riyesh, N. Virmani, M. Hussain, R. K. Singh and B. N. Tripathi, Abundance of Antibiotic Resistance Genes in Environmental Bacteriophages, J. Gen. Virol., 2016, 97(12), 3458–3466,  DOI:10.1099/jgv.0.000639.
57. E. Marti, E. Variatza and J. L. Balcázar, Bacteriophages as a Reservoir of Extended-Spectrum β -Lactamase and Fluoroquinolone Resistance Genes in the Environment, Clin. Microbiol. Infect., 2014, 20(7), O456–O459,  DOI:10.1111/1469-0691.12446.
58. T. M. Nolan, L. Sala-Comorera, L. J. Reynolds, N. A. Martin, J. H. Stephens, G. M. P. O'Hare, J. J. O'Sullivan and W. G. Meijer, Bacteriophages from Faecal Contamination Are an Important Reservoir for AMR in Aquatic Environments, Sci. Total Environ., 2023, 900, 165490,  DOI:10.1016/j.scitotenv.2023.165490.
59. T. Billard-Pomares, S. Fouteau, M. E. Jacquet, D. Roche, V. Barbe, M. Castellanos, J. Y. Bouet, S. Cruveiller, C. Médigue, J. Blanco, O. Clermont, E. Denamur and C. Branger, Characterization of a P1-Like Bacteriophage Carrying an SHV-2 Extended-Spectrum β-Lactamase from an Escherichia Coli Strain, Antimicrob. Agents Chemother., 2014, 58(11), 6550–6557,  DOI:10.1128/AAC.03183-14.
60. K. Zhang, R. Xin, Z. Zhao, W. Li, Y. Wang, Q. Wang, Z. Niu and Y. Zhang, Mobile Genetic Elements Are the Major Driver of High Antibiotic Resistance Genes Abundance in the Upper Reaches of Huaihe River Basin, J. Hazard. Mater., 2021, 401, 123271,  DOI:10.1016/j.jhazmat.2020.123271.
61. I. Balta, J. Lemon, A. Gadaj, I. Cretescu, D. Stef, I. Pet, L. Stef, D. McCleery, A. Douglas and N. Corcionivoschi, The Interplay between Antimicrobial Resistance, Heavy Metal Pollution, and the Role of Microplastics, Front. Microbiol., 2025, 16, 1550587,  DOI:10.3389/fmicb.2025.1550587.
62. T. Schwartz, W. Kohnen, B. Jansen and U. Obst, Detection of Antibiotic-Resistant Bacteria and Their Resistance Genes in Wastewater, Surface Water, and Drinking Water Biofilms, FEMS Microbiol. Ecol., 2003, 43(3), 325–335,  DOI:10.1111/j.1574-6941.2003.tb01073.x.
63. N. Gross, J. Muhvich, C. Ching, B. Gomez, E. Horvath, Y. Nahum and M. H. Zaman, Effects of Microplastic Concentration, Composition, and Size on Escherichia Coli Biofilm-Associated Antimicrobial Resistance, Appl. Environ. Microbiol., 2025, 91(4), e02282–24,  DOI:10.1128/aem.02282-24.
64. L. Proia, D. Von Schiller, A. Sànchez-Melsió, S. Sabater, C. M. Borrego, S. Rodríguez-Mozaz and J. L. Balcázar, Occurrence and Persistence of Antibiotic Resistance Genes in River Biofilms after Wastewater Inputs in Small Rivers, Environ. Pollut., 2016, 210, 121–128,  DOI:10.1016/j.envpol.2015.11.035.
65. B. Li, Y. Qiu, J. Zhang, X. Huang, H. Shi and H. Yin, Real-Time Study of Rapid Spread of Antibiotic Resistance Plasmid in Biofilm Using Microfluidics, Environ. Sci. Technol., 2018, 52(19), 11132–11141,  DOI:10.1021/acs.est.8b03281.
66. D. W. Graham, G. Bergeron, M. W. Bourassa, J. Dickson, F. Gomes, A. Howe, L. H. Kahn, P. S. Morley, H. M. Scott, S. Simjee, R. S. Singer, T. C. Smith, C. Storrs and T. E. Wittum, Complexities in Understanding Antimicrobial Resistance across Domesticated Animal, Human, and Environmental Systems, Ann. N. Y. Acad. Sci., 2019, 1441(1), 17–30,  DOI:10.1111/nyas.14036.
67. H. Liao, X. Li, Q. Yang, Y. Bai, P. Cui, C. Wen, C. Liu, Z. Chen, J. Tang, J. Che, Z. Yu, S. Geisen, S. Zhou, V.-P. Friman and Y.-G. Zhu, Herbicide Selection Promotes Antibiotic Resistance in Soil Microbiomes, Mol. Biol. Evol., 2021, 38(6), 2337–2350,  DOI:10.1093/molbev/msab029.
68. H. Bai, L.-Y. He, D.-L. Wu, F.-Z. Gao, M. Zhang, H.-Y. Zou, M.-S. Yao and G.-G. Ying, Spread of Airborne Antibiotic Resistance from Animal Farms to the Environment: Dispersal Pattern and Exposure Risk, Environ. Int., 2022, 158, 106927,  DOI:10.1016/j.envint.2021.106927.
69. P. Munk, V. D. Andersen, L. De Knegt, M. S. Jensen, B. E. Knudsen, O. Lukjancenko, H. Mordhorst, J. Clasen, Y. Agersø, A. Folkesson, S. J. Pamp, H. Vigre and F. M. Aarestrup, A Sampling and Metagenomic Sequencing-Based Methodology for Monitoring Antimicrobial Resistance in Swine Herds, J. Antimicrob. Chemother., 2017, 72(2), 385–392,  DOI:10.1093/jac/dkw415.
70. M.-S. Zhang, W. Li, W.-G. Zhang, Y.-T. Li, J.-Y. Li and Y. Gao, Agricultural Land-Use Change Exacerbates the Dissemination of Antibiotic Resistance Genes via Surface Runoffs in Lake Tai Basin, China, Ecotoxicol. Environ. Saf., 2021, 220, 112328,  DOI:10.1016/j.ecoenv.2021.112328.
71. G. Rasschaert, D. Van Elst, L. Colson, L. Herman, H. C. De Carvalho Ferreira, J. Dewulf, J. Decrop, J. Meirlaen, M. Heyndrickx and E. Daeseleire, Antibiotic Residues and Antibiotic-Resistant Bacteria in Pig Slurry Used to Fertilize Agricultural Fields, Antibiotics, 2020, 9(1), 34,  DOI:10.3390/antibiotics9010034.
72. Z. Yu, L. Gunn, P. Wall and S. Fanning, Antimicrobial Resistance and Its Association with Tolerance to Heavy Metals in Agriculture Production, Food Microbiol., 2017, 64, 23–32,  DOI:10.1016/j.fm.2016.12.009.
73. X. Qian, J. Gu, W. Sun, X.-J. Wang, J.-Q. Su and R. Stedfeld, Diversity, Abundance, and Persistence of Antibiotic Resistance Genes in Various Types of Animal Manure Following Industrial Composting, J. Hazard. Mater., 2018, 344, 716–722,  DOI:10.1016/j.jhazmat.2017.11.020.
74. Y. M. Zou, Y. Ma, J. H. Liu, J. Shi, T. Fan, Y. Y. Shan, H. P. Yao and Y. L. Dong, Trends and Correlation of Antibacterial Usage and Bacterial Resistance: Time Series Analysis for Antibacterial Stewardship in a Chinese Teaching Hospital (2009–2013), Eur. J. Clin. Microbiol. Infect. Dis., 2015, 34(4), 795–803,  DOI:10.1007/s10096-014-2293-6.
75. A. A. Witney, K. A. Gould, C. F. Pope, F. Bolt, N. G. Stoker, M. D. Cubbon, C. R. Bradley, A. Fraise, A. S. Breathnach, P. D. Butcher, T. D. Planche and J. Hinds, Genome Sequencing and Characterization of an Extensively Drug-Resistant Sequence Type 111 Serotype O12 Hospital Outbreak Strain of Pseudomonas Aeruginosa, Clin. Microbiol. Infect., 2014, 20(10), O609–O618,  DOI:10.1111/1469-0691.12528.
76. S. Mishra, S. Upadhyay, M. R. Sen, A. P. Maurya, D. Choudhury and A. Bhattacharjee, Genetic Acquisition of NDM Gene Offers Sustainability among Clinical Isolates of Pseudomonas Aeruginosa in Clinical Settings, PLoS One, 2015, 10(1), e0116611,  DOI:10.1371/journal.pone.0116611.
77. E. Kosykowska, K. Szymanek-Majchrzak, S. Walter De Walthoffen, R. Izdebski, A. Mlynarczyk, M. Ciszek, A. Chmura, M. Durlik, L. Paczek, D. Deborska-Materkowska, A. Sawicka-Grzelak and G. Mlynarczyk, Molecular Analysis of Carbapenem-Resistant Strains of Pseudomonas Aeruginosa Isolated From Patients Hospitalized in Various Transplantation Wards Between 2008 and 2011, Transplant. Proc., 2014, 46(8), 2576–2578,  DOI:10.1016/j.transproceed.2014.08.027.
78. J. H. Jorgensen, M. J. Ferraro, M. L. McElmeel, J. Spargo, J. M. Swenson and F. C. Tenover, Detection of Penicillin and Extended-Spectrum Cephalosporin Resistance among Streptococcus Pneumoniae Clinical Isolates by Use of the E Test, J. Clin. Microbiol., 1994, 32(1), 159–163,  DOI:10.1128/jcm.32.1.159-163.1994.
79. I. Barišić, D. Mitteregger, A. M. Hirschl, C. Noehammer and H. Wiesinger-Mayr, High Diversity of Beta-Lactamases in the General Hospital Vienna Verified by Whole Genome Sequencing and Statistical Analysis, Infect., Genet. Evol., 2014, 27, 408–417,  DOI:10.1016/j.meegid.2014.08.014.
80. A. Ulvi, S. Aydın and M. E. Aydın, Fate of Selected Pharmaceuticals in Hospital and Municipal Wastewater Effluent: Occurrence, Removal, and Environmental Risk Assessment, Environ. Sci. Pollut. Res., 2022, 29(50), 75609–75625,  DOI:10.1007/s11356-022-21131-y.
81. F. S. Souza and L. A. Féris, Hospital and Municipal Wastewater: Identification of Relevant Pharmaceutical Compounds, Water Environ. Res., 2016, 88(9), 871–877,  DOI:10.2175/106143016X14609975747603.
82. R. Silvester, W. B. Perry, G. Webster, L. Rushton, A. Baldwin, D. A. Pass, N. Healey, K. Farkas, N. Craine, G. Cross, P. Kille, A. J. Weightman and D. L. Jones, Metagenomics Unveils the Role of Hospitals and Wastewater Treatment Plants on the Environmental Burden of Antibiotic Resistance Genes and Opportunistic Pathogens, Sci. Total Environ., 2025, 961, 178403,  DOI:10.1016/j.scitotenv.2025.178403.
83. M. R. Mulvey, E. Bryce, D. A. Boyd, M. Ofner-Agostini, A. M. Land, A. E. Simor and S. Paton, Molecular Characterization of Cefoxitin-Resistant Escherichia Coli from Canadian Hospitals, Antimicrob. Agents Chemother., 2005, 49(1), 358–365,  DOI:10.1128/AAC.49.1.358-365.2005.
84. D. Hocquet, A. Muller and X. Bertrand, What Happens in Hospitals Does Not Stay in Hospitals: Antibiotic-Resistant Bacteria in Hospital Wastewater Systems, J. Hosp. Infect., 2016, 93(4), 395–402,  DOI:10.1016/j.jhin.2016.01.010.
85. A. M. M. A. Chowdhury and K. N. Uddin, Analysis of the Occurrence of Antibiotic Resistant Bacteria in the Hospital's Effluent and Its Receiving Environment, Microbiol. Insights, 2022, 15, 11786361221078211,  DOI:10.1177/11786361221078211.
86. S. Yang, L. Li, X. Peng and L. Song, Leachate Microbiome Profile Reveals Bacteria, Archaea and Eukaryote Dynamics and Methanogenic Function during Solid Waste Decomposition, Bioresour. Technol., 2021, 320, 124359,  DOI:10.1016/j.biortech.2020.124359.
87. D. Wu, X.-H. Huang, J.-Z. Sun, D. W. Graham and B. Xie, Antibiotic Resistance Genes and Associated Microbial Community Conditions in Aging Landfill Systems, Environ. Sci. Technol., 2017, 51(21), 12859–12867,  DOI:10.1021/acs.est.7b03797.
88. Y. Wang, R. Zhang, Y. Lei and L. Song, Antibiotic Resistance Genes in Landfill Leachates from Seven Municipal Solid Waste Landfills: Seasonal Variations, Hosts, and Risk Assessment, Sci. Total Environ., 2022, 853, 158677,  DOI:10.1016/j.scitotenv.2022.158677.
89. S. Threedeach, W. Chiemchaisri, T. Watanabe, C. Chiemchaisri, R. Honda and K. Yamamoto, Antibiotic Resistance of Escherichia Coli in Leachates from Municipal Solid Waste Landfills: Comparison between Semi-Aerobic and Anaerobic Operations, Bioresour. Technol., 2012, 113, 253–258,  DOI:10.1016/j.biortech.2012.01.086.
90. A. Talat, Y. Bashir, N. Khalil, C. L. Brown, D. Gupta and A. U. Khan, Antimicrobial Resistance Transmission in the Environmental Settings through Traditional and UV-Enabled Advanced Wastewater Treatment Plants: A Metagenomic Insight, Environ. Microbiome, 2025, 20(1), 27,  DOI:10.1186/s40793-024-00658-2.
91. L. Rizzo, C. Manaia, C. Merlin, T. Schwartz, C. Dagot, M. C. Ploy, I. Michael and D. Fatta-Kassinos, Urban Wastewater Treatment Plants as Hotspots for Antibiotic Resistant Bacteria and Genes Spread into the Environment, A Review, Sci. Total Environ., 2013, 447, 345–360,  DOI:10.1016/j.scitotenv.2013.01.032.
92. A. Miłobedzka, C. Ferreira, I. Vaz-Moreira, D. Calderón-Franco, A. Gorecki, S. Purkrtova, J. Bartacek, L. Dziewit, C. M. Singleton, P. H. Nielsen, D. G. Weissbrodt and C. M. Manaia, Monitoring Antibiotic Resistance Genes in Wastewater Environments: The Challenges of Filling a Gap in the One-Health Cycle, J. Hazard. Mater., 2022, 424, 127407,  DOI:10.1016/j.jhazmat.2021.127407.
93. B. Madhogaria, S. Banerjee, A. Kundu and P. Dhak, Efficacy of New Generation Biosorbents for the Sustainable Treatment of Antibiotic Residues and Antibiotic Resistance Genes from Polluted Waste Effluent, Infect. Med., 2024, 3(1), 100092,  DOI:10.1016/j.imj.2024.100092.
94. A. Karkman, T. T. Do, F. Walsh and M. P. J. Virta, Antibiotic-Resistance Genes in Waste Water, Trends Microbiol., 2018, 26(3), 220–228,  DOI:10.1016/j.tim.2017.09.005.
95. J. Lee, S. G. Shin, H. M. Jang, Y. B. Kim, J. Lee and Y. M. Kim, Characterization of Antibiotic Resistance Genes in Representative Organic Solid Wastes: Food Waste-Recycling Wastewater, Manure, and Sewage Sludge, Sci. Total Environ., 2017, 579, 1692–1698,  DOI:10.1016/j.scitotenv.2016.11.187.
96. W. Gunawardana, R. S. Kalupahana, S. A. Kottawatta, A. Gamage and O. Merah, A Review of the Dissemination of Antibiotic Resistance through Wastewater Treatment Plants: Current Situation in Sri Lanka and Future Perspectives, Life, 2024, 14(9), 1065,  DOI:10.3390/life14091065.
97. World Health Organization (WHO), WHO Fungal Priority Pathogens List to Guide Research, Development and Public Health Action, 1st edn, World Health Organization (WHO), Geneva, 2022.
98. I. Capua and G. Cattoli, One Health (r)Evolution: Learning from the Past to Build a New Future, Viruses, 2018, 10(12), 725,  DOI:10.3390/v10120725.
99. C. Wu, J. Ong, C. C. Astbury, K. M. Lee, Z. Shi, Z. Gong, A. M. Viens, S. Desai, P. Tsasis and T. L. Penney, Globalizing One Health Requires Consistent and Culturally Appropriate Translation of the Term across Languages, BioScience, 2025, biaf161,  DOI:10.1093/biosci/biaf161.
100. C. Dye, One Health as a Catalyst for Sustainable Development, Nat. Microbiol., 2022, 7(4), 467–468,  DOI:10.1038/s41564-022-01076-1.
101. J. S. Mackenzie and M. Jeggo, The One Health Approach—Why Is It So Important?, Trop. Med. Infect. Dis., 2019, 4(2), 88,  DOI:10.3390/tropicalmed4020088.
102. L. H. Taylor, S. M. Latham and M. E. J. Woolhouse, Risk Factors for Human Disease Emergence, Philos. Trans. R. Soc., B, 2001, 356(1411), 983–989,  DOI:10.1098/rstb.2001.0888.
103. J. S. Mackenzie, and M. McKinnon and M. Jeggo, One Health: From Concept to Practice, in Confronting Emerging Zoonoses, ed. A. Yamada, L. H. Kahn, B. Kaplan, T. P. Monath, J. Woodall and L. Conti, Springer Japan, Tokyo, 2014, pp. 163–189.  DOI:10.1007/978-4-431-55120-1_8.
104. S. Mubareka, J. Amuasi, A. Banerjee, H. Carabin, J. C. Jack, C. Jardine, B. Jaroszewicz, G. Keefe, J. Kotwa, S. Kutz, D. McGregor, A. Mease, L. Nicholson, K. Nowak, B. Pickering, M. G. Reed, J. Saint-Charles, K. Simonienko, T. Smith, J. S. Weese and E. J. Parmley, Strengthening a One Health Approach to Emerging Zoonoses, FACETS, 2023, 8, 1–64,  DOI:10.1139/facets-2021-0190.
105. M. Pollowitz, C. Allick, K. B. Campbell, N. L. K. Ellison, G. Perez-Aguilar, M. Vera, V. Ramirez, D. Nadal and J. Meisner, One Health, Many Perspectives: Exploring Indigenous and Western Epistemologies, CABI One Health, 2024, 3(1) DOI:10.1079/cabionehealth.2024.0015.
106. K. Hueffer, M. Ehrlander, K. Etz and A. Reynolds, One Health in the Circumpolar North, Int. J. Circumpolar Health, 2019, 78(1), 1607502,  DOI:10.1080/22423982.2019.1607502.
107. L. F. Lavallee and J. M. Poole, Beyond Recovery: Colonization, Health and Healing for Indigenous People in Canada, Int. J. Ment. Health Addict., 2010, 8(2), 271–281,  DOI:10.1007/s11469-009-9239-8.
108. F. Berkes, Indigenous Ways of Knowing and the Study of Environmental Change, J. R. Soc. N. Z., 2009, 39(4), 151–156,  DOI:10.1080/03014220909510568.
109. S. A. Hillier, A. Taleb, E. Chaccour and C. Aenishaenslin, Examining the Concept of One Health for Indigenous Communities: A Systematic Review, One Health, 2021, 12, 100248,  DOI:10.1016/j.onehlt.2021.100248.
110. D. Destoumieux-Garzón, P. Mavingui, G. Boetsch, J. Boissier, F. Darriet, P. Duboz, C. Fritsch, P. Giraudoux, F. Le Roux, S. Morand, C. Paillard, D. Pontier, C. Sueur and Y. Voituron, The One Health Concept: 10 Years Old and a Long Road Ahead, Front. Vet. Sci., 2018, 5, 14,  DOI:10.3389/fvets.2018.00014.
111. Communications, Values, Haudenosaunee Confederacy, https://www.haudenosauneeconfederacy.com/values/ (accessed 2025-10-30).
112. J. C. Jack, J. Gonet, A. Mease and K. Nowak, Traditional Knowledge Underlies One Health, Science, 2020, 369(6511), 1576,  DOI:10.1126/science.abe2401.
113. C. Bartlett, M. Marshall and A. Marshall, Two-Eyed Seeing and Other Lessons Learned within a Co-Learning Journey of Bringing Together Indigenous and Mainstream Knowledges and Ways of Knowing, J. Environ. Stud. Sci., 2012, 2(4), 331–340,  DOI:10.1007/s13412-012-0086-8.
114. M. Michie, M. Hogue and J. Rioux, The Application of Both-Ways and Two-Eyed Seeing Pedagogy: Reflections on Engaging and Teaching Science to Post-Secondary Indigenous Students, Res. Sci. Educ., 2018, 48(6), 1205–1220,  DOI:10.1007/s11165-018-9775-y.
115. G. C. Borg and T. Kumblathan, Bridging Indigenous Ways of Knowing with Western Science Pedagogy in STEM Education, Anal. Chem., 2025, 97(19), 10097–10098,  DOI:10.1021/acs.analchem.5c02421.
116. One Health High-Level Expert Panel (OHHLEP), W. B. Adisasmito, S. Almuhairi, C. B. Behravesh, P. Bilivogui, S. A. Bukachi, N. Casas, N. Cediel-Becerra, D. F. Charron, A. Chaudhary, J. R. Ciacci-Zanella, A. A. Cunningham, O. Dar, N. Debnath, B. Dungu, E. Farag, G. F. Gao, D. T. S. Hayman, M. Khaitsa, M. P. G. Koopmans, C. Machalaba, J. S. Mackenzie, W. Markotter, T. C. Mettenleiter, S. Morand, V. Smolenskiy and L. Zhou, One Health: A New Definition for a Sustainable and Healthy Future, PLoS Pathog., 2022, 18(6), e1010537,  DOI:10.1371/journal.ppat.1010537.
117. S. S. Sambaza and N. Naicker, Contribution of Wastewater to Antimicrobial Resistance: A Review Article, J. Global Antimicrob. Resist., 2023, 34, 23–29,  DOI:10.1016/j.jgar.2023.05.010.
118. S. M. Zainab, M. Junaid, N. Xu and R. N. Malik, Antibiotics and Antibiotic Resistant Genes (ARGs) in Groundwater: A Global Review on Dissemination, Sources, Interactions, Environmental and Human Health Risks, Water Res., 2020, 187, 116455,  DOI:10.1016/j.watres.2020.116455.
119. Y. Yang, S. Cai, C. Mo, J. Dong, S. Chen and Z. Wen, Profiles of Antibiotic Resistome Risk in Diverse Water Environments, Commun. Earth Environ., 2025, 6(1), 158,  DOI:10.1038/s43247-025-02139-x.
120. C. A. Engemann, P. L. Keen, C. W. Knapp, K. J. Hall and D. W. Graham, Fate of Tetracycline Resistance Genes in Aquatic Systems: Migration from the Water Column to Peripheral Biofilms, Environ. Sci. Technol., 2008, 42(14), 5131–5136,  DOI:10.1021/es800238e.
121. B. L. Coleman, M. I. Salvadori, A. J. McGEER, K. A. Sibley, N. F. Neumann, S. J. Bondy, I. A. Gutmanis, S. A. McEWEN, M. Lavoie, D. Strong, I. Johnson, F. B. Jamieson, M. Louie and ARO Water Study Group, The Role of Drinking Water in the Transmission of Antimicrobial-Resistant E. Coli, Epidemiol. Infect., 2012, 140(4), 633–642,  DOI:10.1017/S0950268811001038.
122. H.-C. Flemming, J. Wingender, U. Szewzyk, P. Steinberg, S. A. Rice and S. Kjelleberg, Biofilms: An Emergent Form of Bacterial Life, Nat. Rev. Microbiol., 2016, 14(9), 563–575,  DOI:10.1038/nrmicro.2016.94.
123. W. Costerton, R. Veeh, M. Shirtliff, M. Pasmore, C. Post and G. Ehrlich, The Application of Biofilm Science to the Study and Control of Chronic Bacterial Infections, J. Clin. Invest., 2003, 112(10), 1466–1477,  DOI:10.1172/JCI200320365.
124. G. M. Teitzel and M. R. Parsek, Heavy Metal Resistance of Biofilm and Planktonic Pseudomonas Aeruginosa, Appl. Environ. Microbiol., 2003, 69(4), 2313–2320,  DOI:10.1128/AEM.69.4.2313-2320.2003.
125. K. K. Jefferson, D. A. Goldmann and G. B. Pier, Use of Confocal Microscopy To Analyze the Rate of Vancomycin Penetration through Staphylococcus Aureus Biofilms, Antimicrob. Agents Chemother., 2005, 49(6), 2467–2473,  DOI:10.1128/AAC.49.6.2467-2473.2005.
126. C. F. García, M. Kretschmer, C. N. Lozano-Andrade, M. Schönleitner, A. Dragoŝ, ÁT Kovács and O. Lieleg, Metal Ions Weaken the Hydrophobicity and Antibiotic Resistance of Bacillus Subtilis NCIB 3610 Biofilms, npj Biofilms Microbiomes, 2020, 6(1) DOI:10.1038/s41522-019-0111-8.
127. H.-W. Hu, J.-T. Wang, J. Li, X.-Z. Shi, Y.-B. Ma, D. Chen and J.-Z. He, Long-Term Nickel Contamination Increases the Occurrence of Antibiotic Resistance Genes in Agricultural Soils, Environ. Sci. Technol., 2017, 51(2), 790–800,  DOI:10.1021/acs.est.6b03383.
128. B. F. Gillieatt and N. V. Coleman, Unravelling the Mechanisms of Antibiotic and Heavy Metal Resistance Co-Selection in Environmental Bacteria, FEMS Microbiol. Rev., 2024, 48(4), fuae017,  DOI:10.1093/femsre/fuae017.
129. C. Baker-Austin, M. S. Wright, R. Stepanauskas and J. V. McArthur, Co-Selection of Antibiotic and Metal Resistance, Trends Microbiol., 2006, 14(4), 176–182,  DOI:10.1016/j.tim.2006.02.006.
130. Y. Zhang, A. Z. Gu, T. Cen, X. Li, M. He, D. Li and J. Chen, Sub-Inhibitory Concentrations of Heavy Metals Facilitate the Horizontal Transfer of Plasmid-Mediated Antibiotic Resistance Genes in Water Environment, Environ. Pollut., 2018, 237, 74–82,  DOI:10.1016/j.envpol.2018.01.032.
131. D. L. Morris, DNA-Bound Metal Ions: Recent Developments, Biomol. Concepts, 2014, 5(5), 397–407,  DOI:10.1515/bmc-2014-0021.
132. A. Kuffel, A. Gray and N. N. Daeid, Impact of Metal Ions on PCR Inhibition and RT-PCR Efficiency, Int. J. Leg. Med., 2021, 135(1), 63–72,  DOI:10.1007/s00414-020-02363-4.
133. A. Wedrychowski, W. N. Schmidt and L. S. Hnilica, The in Vivo Cross-Linking of Proteins and DNA by Heavy Metals, J. Biol. Chem., 1986, 261(7), 3370–3376,  DOI:10.1016/S0021-9258(17)35792-7.
134. L. G. Combs, J. E. Warren, V. Huynh, J. Castaneda, T. D. Golden and R. K. Roby, The Effects of Metal Ion PCR Inhibitors on Results Obtained with the Quantifiler ® Human DNA Quantification Kit, Forensic Sci. Int.:Genet., 2015, 19, 180–189,  DOI:10.1016/j.fsigen.2015.06.013.
135. K. L. Opel, D. Chung and B. R. McCord, A Study of PCR Inhibition Mechanisms Using Real Time PCR*^,†, J. Forensic Sci., 2010, 55(1), 25–33,  DOI:10.1111/j.1556-4029.2009.01245.x.
136. J.-L. Sagripanti, P. L. Goering and A. Lamanna, Interaction of Copper with DNA and Antagonism by Other Metals, Toxicol. Appl. Pharmacol., 1991, 110(3), 477–485,  DOI:10.1016/0041-008X(91)90048-J.
137. Y. Zhang, J. Lu, J. Wu, J. Wang and Y. Luo, Potential Risks of Microplastics Combined with Superbugs: Enrichment of Antibiotic Resistant Bacteria on the Surface of Microplastics in Mariculture System, Ecotoxicol. Environ. Saf., 2020, 187, 109852,  DOI:10.1016/j.ecoenv.2019.109852.
138. E. M. Stevenson, O. Rushby-Jones, A. Buckling, M. Cole, P. K. Lindeque and A. K. Murray, Selective Colonization of Microplastics, Wood and Glass by Antimicrobial-Resistant and Pathogenic Bacteria, Microbiology, 2024, 170(10) DOI:10.1099/mic.0.001506.
139. R. E. Moore, B. C. Millar and J. E. Moore, Antimicrobial Resistance (AMR) and Marine Plastics: Can Food Packaging Litter Act as a Dispersal Mechanism for AMR in Oceanic Environments?, Mar. Pollut. Bull., 2020, 150, 110702,  DOI:10.1016/j.marpolbul.2019.110702.
140. A. McCormick, T. J. Hoellein, S. A. Mason, J. Schluep and J. J. Kelly, Microplastic Is an Abundant and Distinct Microbial Habitat in an Urban River, Environ. Sci. Technol., 2014, 48(20), 11863–11871,  DOI:10.1021/es503610r.
141. P. Laganà, G. Caruso, I. Corsi, E. Bergami, V. Venuti, D. Majolino, R. La Ferla, M. Azzaro and S. Cappello, Do Plastics Serve as a Possible Vector for the Spread of Antibiotic Resistance? First Insights from Bacteria Associated to a Polystyrene Piece from King George Island (Antarctica), Int. J. Hyg. Environ. Health, 2019, 222(1), 89–100,  DOI:10.1016/j.ijheh.2018.08.009.
142. N. Gross, J. Muhvich, C. Ching, B. Gomez, E. Horvath, Y. Nahum and M. H. Zaman, Microplastics as a Novel Facilitator for Antimicrobial Resistance: Effects of Concentration, Composition, and Size on Escherichia Coli Multidrug Resistance, bioRxiv, 2024 DOI:10.1101/2024.08.01.606221.
143. P. Cholewińska, K. Wojnarowski, N. Szeligowska, P. Pokorny, W. Hussein, Y. Hasegawa, W. Dobicki and D. Palić, Presence of Microplastic Particles Increased Abundance of Pathogens and Antimicrobial Resistance Genes in Microbial Communities from the Oder River Water and Sediment, Sci. Rep., 2025, 15(1), 16338,  DOI:10.1038/s41598-025-01136-6.
144. J. Bowley, C. Baker-Austin, A. Porter, R. Hartnell and C. Lewis, Oceanic Hitchhikers – Assessing Pathogen Risks from Marine Microplastic, Trends Microbiol., 2021, 29(2), 107–116,  DOI:10.1016/j.tim.2020.06.011.
145. M. Arias-Andres, U. Klümper, K. Rojas-Jimenez and H.-P. Grossart, Microplastic Pollution Increases Gene Exchange in Aquatic Ecosystems, Environ. Pollut., 2018, 237, 253–261,  DOI:10.1016/j.envpol.2018.02.058.
146. H. Dong, Y. Chen, J. Wang, Y. Zhang, P. Zhang, X. Li, J. Zou and A. Zhou, Interactions of Microplastics and Antibiotic Resistance Genes and Their Effects on the Aquaculture Environments, J. Hazard. Mater., 2021, 403, 123961,  DOI:10.1016/j.jhazmat.2020.123961.
147. G. Milani, C. Cortimiglia, M. V. Belloso Daza, E. Greco, D. Bassi and P. S. Cocconcelli, Microplastic-Mediated Transfer of Tetracycline Resistance: Unveiling the Role of Mussels in Marine Ecosystems, Antibiotics, 2024, 13(8), 727,  DOI:10.3390/antibiotics13080727.
148. J. Shi, D. Wu, Y. Su and B. Xie, (Nano)Microplastics Promote the Propagation of Antibiotic Resistance Genes in Landfill Leachate, Environ. Sci. Nano, 2020, 7(11), 3536–3546,  10.1039/D0EN00511H.
149. S. Dai, R. Ye, J. Huang, B. Wang, Z. Xie, X. Ou, N. Yu, C. Huang, Y. Hua, R. Zhou and B. Tian, Distinct Lipid Membrane Interaction and Uptake of Differentially Charged Nanoplastics in Bacteria, J. Nanobiotechnol., 2022, 20(1), 191,  DOI:10.1186/s12951-022-01321-z.
150. L. Liu, Y. Sun, S. Du, Y. Li and J. Wang, Nanoplastics Promote the Dissemination of Antibiotic Resistance Genes and Diversify Their Bacterial Hosts in Soil, Eco-Environ. & Health, 2024, 3(1), 1–10,  DOI:10.1016/j.eehl.2023.09.005.
151. Y. Zha, Z. Li, Z. Zhong, Y. Ruan, L. Sun, F. Zuo, L. Li and S. Hou, Size-Dependent Enhancement on Conjugative Transfer of Antibiotic Resistance Genes by Micro/Nanoplastics, J. Hazard. Mater., 2022, 431, 128561,  DOI:10.1016/j.jhazmat.2022.128561.
152. T. F. Lins, A. M. O'Brien, T. Kose, C. M. Rochman and D. Sinton, Toxicity of Nanoplastics to Zooplankton Is Influenced by Temperature, Salinity, and Natural Particulate Matter, Environ. Sci. Nano, 2022, 9(8), 2678–2690,  10.1039/D2EN00123C.
153. Q. E. Yang, Z. Lin, D. Gan, M. Li, X. Liu, S. Zhou and T. R. Walsh, Microplastics Mediates the Spread of Antimicrobial Resistance Plasmids via Modulating Conjugal Gene Expression, Environ. Int., 2025, 195, 109261,  DOI:10.1016/j.envint.2025.109261.
154. J. Wang, D. Ma, K. Feng, Y. Lou, H. Zhou, B. Liu, G. Xie, N. Ren and D. Xing, Polystyrene Nanoplastics Shape Microbiome and Functional Metabolism in Anaerobic Digestion, Water Res., 2022, 219, 118606,  DOI:10.1016/j.watres.2022.118606.
155. L. Wu, K. Patel, M. Zandieh and J. Liu, Promotion of DNA Adsorption onto Microplastics by Transition Metal Ions, Microplastics, 2023, 2(1), 158–167,  DOI:10.3390/microplastics2010012.
156. R. U. Halden, A. E. Lindeman, A. E. Aiello, D. Andrews, W. A. Arnold, P. Fair, R. E. Fuoco, L. A. Geer, P. I. Johnson, R. Lohmann, K. McNeill, V. P. Sacks, T. Schettler, R. Weber, R. T. Zoeller and A. Blum, The Florence Statement on Triclosan and Triclocarban, Environ. Health Perspect., 2017, 125(6), 064501,  DOI:10.1289/EHP1788.
157. B. Drury, J. Scott, E. J. Rosi-Marshall and J. J. Kelly, Triclosan Exposure Increases Triclosan Resistance and Influences Taxonomic Composition of Benthic Bacterial Communities, Environ. Sci. Technol., 2013, 47(15), 8923–8930,  DOI:10.1021/es401919k.
158. H. Barrett, J. Sun, Y. Gong, P. Yang, C. Hao, J. Verreault, Y. Zhang and H. Peng, Triclosan Is the Predominant Antibacterial Compound in Ontario Sewage Sludge, Environ. Sci. Technol., 2022, 56(21), 14923–14936,  DOI:10.1021/acs.est.2c00406.
159. J. Lu, M. Jin, S. H. Nguyen, L. Mao, J. Li, L. J. M. Coin, Z. Yuan and J. Guo, Non-Antibiotic Antimicrobial Triclosan Induces Multiple Antibiotic Resistance through Genetic Mutation, Environ. Int., 2018, 118, 257–265,  DOI:10.1016/j.envint.2018.06.004.
160. P. J. McNamara, T. M. LaPara and P. J. Novak, The Impacts of Triclosan on Anaerobic Community Structures, Function, and Antimicrobial Resistance, Environ. Sci. Technol., 2014, 48(13), 7393–7400,  DOI:10.1021/es501388v.
161. C. G. Daughton and T. A. Ternes, Pharmaceuticals and Personal Care Products in the Environment: Agents of Subtle Change?, Environ. Health Perspect., 1999, 107(suppl 6), 907–938,  DOI:10.1289/ehp.99107s6907.
162. E. M. Hartmann, R. Hickey, T. Hsu, C. M. Betancourt-Román, J. Chen, R. Schwager, J. Kline, G. Z. Brown, R. U. Halden, C. Huttenhower and J. L. Green, Antimicrobial Chemicals Are Associated with Elevated Antibiotic Resistance Genes in the Indoor Dust Microbiome, Environ. Sci. Technol., 2016, 50(18), 9807–9815,  DOI:10.1021/acs.est.6b00262.
163. V. Divya, B. D. Vishwajit, S. A. Adhoni, S. Manawadi, V. Rajeshwar, M. Khalid, S. Wahab, S. J. Patil, K. M. Anilkumar and H. P. Shivaraju, Impacts of Pharmaceutical and Personal Care Products Contamination as Emerging Contaminants in Urban Ecosystem: Emerging Risks and Future Challenges, Environ. Sci. Eur., 2025, 37(1), 170,  DOI:10.1186/s12302-025-01210-w.
164. J. Lu, Y. Wang, S. Zhang, P. Bond, Z. Yuan and J. Guo, Triclosan at Environmental Concentrations Can Enhance the Spread of Extracellular Antibiotic Resistance Genes through Transformation, Sci. Total Environ., 2020, 713, 136621,  DOI:10.1016/j.scitotenv.2020.136621.
165. J. Lu, Y. Wang, J. Li, L. Mao, S. H. Nguyen, T. Duarte, L. Coin, P. Bond, Z. Yuan and J. Guo, Triclosan at Environmentally Relevant Concentrations Promotes Horizontal Transfer of Multidrug Resistance Genes within and across Bacterial Genera, Environ. Int., 2018, 121, 1217–1226,  DOI:10.1016/j.envint.2018.10.040.
166. J. Heidler and R. U. Halden, Mass Balance Assessment of Triclosan Removal during Conventional Sewage Treatment, Chemosphere, 2007, 66(2), 362–369,  DOI:10.1016/j.chemosphere.2006.04.066.
167. F. Fan, K. Yan, N. G. Wallis, S. Reed, T. D. Moore, S. F. Rittenhouse, W. E. DeWolf, J. Huang, D. McDevitt, W. H. Miller, M. A. Seefeld, K. A. Newlander, D. R. Jakas, M. S. Head and D. J. Payne, Defining and Combating the Mechanisms of Triclosan Resistance in Clinical Isolates of Staphylococcus Aureus, Antimicrob. Agents Chemother., 2002, 46(11), 3343–3347,  DOI:10.1128/AAC.46.11.3343-3347.2002.
168. J. L. Romero, M. J. Grande Burgos, R. Pérez-Pulido, A. Gálvez and R. Lucas, Resistance to Antibiotics, Biocides, Preservatives and Metals in Bacteria Isolated from Seafoods: Co-Selection of Strains Resistant or Tolerant to Different Classes of Compounds, Front. Microbiol., 2017, 8, 1650,  DOI:10.3389/fmicb.2017.01650.
169. T. Cen, X. Zhang, S. Xie and D. Li, Preservatives Accelerate the Horizontal Transfer of Plasmid-Mediated Antimicrobial Resistance Genes via Differential Mechanisms, Environ. Int., 2020, 138, 105544,  DOI:10.1016/j.envint.2020.105544.
170. N. Nordholt, O. Kanaris, S. B. I. Schmidt and F. Schreiber, Persistence against Benzalkonium Chloride Promotes Rapid Evolution of Tolerance during Periodic Disinfection, Nat. Commun., 2021, 12(1), 6792,  DOI:10.1038/s41467-021-27019-8.
171. M. Jin, J. Lu, Z. Chen, S. H. Nguyen, L. Mao, J. Li, Z. Yuan and J. Guo, Antidepressant Fluoxetine Induces Multiple Antibiotics Resistance in Escherichia Coli via ROS-Mediated Mutagenesis, Environ. Int., 2018, 120, 421–430,  DOI:10.1016/j.envint.2018.07.046.
172. P. Paíga, M. Correia, M. J. Fernandes, A. Silva, M. Carvalho, J. Vieira, S. Jorge, J. G. Silva, C. Freire and C. Delerue-Matos, Assessment of 83 Pharmaceuticals in WWTP Influent and Effluent Samples by UHPLC-MS/MS: Hourly Variation, Sci. Total Environ., 2019, 648, 582–600,  DOI:10.1016/j.scitotenv.2018.08.129.
173. V. M. Pathak, V. K. Verma, B. S. Rawat, B. Kaur, N. Babu, A. Sharma, S. Dewali, M. Yadav, R. Kumari, S. Singh, A. Mohapatra, V. Pandey, N. Rana and J. M. Cunill, Current Status of Pesticide Effects on Environment, Human Health and It's Eco-Friendly Management as Bioremediation: A Comprehensive Review, Front. Microbiol., 2022, 13, 962619,  DOI:10.3389/fmicb.2022.962619.
174. B. Kurenbach, D. Marjoshi, C. F. Amábile-Cuevas, G. C. Ferguson, W. Godsoe, P. Gibson and J. A. Heinemann, Sublethal Exposure to Commercial Formulations of the Herbicides Dicamba, 2,4-Dichlorophenoxyacetic Acid, and Glyphosate Cause Changes in Antibiotic Susceptibility in Escherichia Coli and Salmonella Enterica Serovar Typhimurium, mBio, 2015, 6(2), e00009–e00015,  DOI:10.1128/mBio.00009-15.
175. N. B. da Costa, M. P. Hébert, V. Fugère, Y. Terrat, G. F. Fussmann, A. Gonzalez and B. J. Shapiro, A Glyphosate-Based Herbicide Cross-Selects for Antibiotic Resistance Genes in Bacterioplankton Communities, mSystems, 2022, 7(2), e01482-21,  DOI:10.1128/msystems.01482-21.
176. A. Almakki, E. Jumas-Bilak, H. Marchandin and P. Licznar-Fajardo, Antibiotic Resistance in Urban Runoff, Sci. Total Environ., 2019, 667, 64–76,  DOI:10.1016/j.scitotenv.2019.02.183.
177. M. Yasemi, A. Jalali, M. Asadzadeh and M. Komijani, Organophosphate Pesticides and Their Potential in the Change of Microbial Population and Frequency of Antibiotic Resistance Genes in Aquatic Environments, Chemosphere, 2025, 376, 144296,  DOI:10.1016/j.chemosphere.2025.144296.
178. H. Zhang, J. Liu, L. Wang and Z. Zhai, Glyphosate Escalates Horizontal Transfer of Conjugative Plasmid Harboring Antibiotic Resistance Genes, Bioengineered, 2021, 12(1), 63–69,  DOI:10.1080/21655979.2020.1862995.
179. K. Rangasamy, M. Athiappan, N. Devarajan, G. Samykannu, J. A. Parray, K. N. Aruljothi, N. Shameem, A. A. Alqarawi, A. Hashem and E. F. Abd-Allah, Pesticide Degrading Natural Multidrug Resistance Bacterial Flora, Microb. Pathog., 2018, 114, 304–310,  DOI:10.1016/j.micpath.2017.12.013.
180. K. Rangasamy, M. Athiappan, N. Devarajan, J. A. Parray, N. Shameem, K. N. Aruljothi, A. Hashem, A. A. Alqarawi and E. F. Abd-Allah, Cloning and Expression of the Organophosphate Pesticide-Degrading α - β Hydrolase Gene in Plasmid pMK-07 to Confer Cross-Resistance to Antibiotics, BioMed Res. Int., 2018, 2018, 1–13,  DOI:10.1155/2018/1535209.
181. L. M. Murray, A. Hayes, J. Snape, B. Kasprzyk-Hordern, W. H. Gaze and A. K. Murray, Co-Selection for Antibiotic Resistance by Environmental Contaminants, npj Antimicrob. Resist., 2024, 2(1), 9,  DOI:10.1038/s44259-024-00026-7.
182. K. Rangasamy, M. Athiappan, N. Devarajan and J. A. Parray, Emergence of Multi Drug Resistance among Soil Bacteria Exposing to Insecticides, Microb. Pathog., 2017, 105, 153–165,  DOI:10.1016/j.micpath.2017.02.011.
183. E. Pagaling, R. Hough, L. Avery, L. Robinson, T. Freitag, M. Coull, X. Zhou, J.-Q. Su, T. Peshkur, Y.-G. Zhu, D. W. Graham and C. W. Knapp, Antibiotic Resistance Patterns in Soils across the Scottish Landscape, Commun. Earth Environ., 2023, 4(1), 403,  DOI:10.1038/s43247-023-01057-0.
184. V. Patyka, N. Buletsa, L. Pasichnyk, N. Zhitkevich, A. Kalinichenko, T. Gnatiuk and L. Butsenko, Specifics of Pesticides Effects on the Phytopathogenic Bacteria, Ecol. Chem. Eng. S, 2016, 23(2), 311–331,  DOI:10.1515/eces-2016-0022.
185. E. A. Beyari, Alternatives to Chemical Pesticides: The Role of Microbial Biocontrol Agents in Phytopathogen Management: A Comprehensive Review, J. Plant Pathol., 2024, 107(1), 291–314,  DOI:10.1007/s42161-024-01808-8.
186. X.-Z. Li and K. Poole, Organic Solvent-Tolerant Mutants of Pseudomonas Aeruginosa Display Multiple Antibiotic Resistance, Can. J. Microbiol., 1999, 45(1), 18–22,  DOI:10.1139/w98-127.
187. A. Rojas, E. Duque, G. Mosqueda, G. Golden, A. Hurtado, J. L. Ramos and A. Segura, Three Efflux Pumps Are Required To Provide Efficient Tolerance to Toluene in Pseudomonas Putida DOT-T1E, J. Bacteriol., 2001, 183(13), 3967–3973,  DOI:10.1128/JB.183.13.3967-3973.2001.
188. X. Yao, F. Tao, K. Zhang, H. Tang and P. Xu, Multiple Roles for Two Efflux Pumps in the Polycyclic Aromatic Hydrocarbon-Degrading Pseudomonas Putida Strain B6–2 (DSM 28064), Appl. Environ. Microbiol., 2017, 83(24), e01882–17,  DOI:10.1128/AEM.01882-17.
189. H. Zou, J. Li, J. Li, X. Shangguan, T. Wu, Q. Li, Z. Wang, H. Zhang, Y. Guan, F. Liu and X. Li, Bilateral Transmission of Plasmid-Mediated Antibiotic Resistance Genes under Polycyclic Aromatic Hydrocarbons Pressure, J. Hazard. Mater., 2025, 496, 139401,  DOI:10.1016/j.jhazmat.2025.139401.
190. B. Chen, R. He, K. Yuan, E. Chen, L. Lin, X. Chen, S. Sha, J. Zhong, L. Lin, L. Yang, Y. Yang, X. Wang, S. Zou and T. Luan, Polycyclic Aromatic Hydrocarbons (PAHs) Enriching Antibiotic Resistance Genes (ARGs) in the Soils, Environ. Pollut., 2017, 220, 1005–1013,  DOI:10.1016/j.envpol.2016.11.047.
191. N. Nagar, H. Saxena, A. Pathak, A. Mishra and K. M. Poluri, A Review on Structural Mechanisms of Protein-Persistent Organic Pollutant (POP) Interactions, Chemosphere, 2023, 332, 138877,  DOI:10.1016/j.chemosphere.2023.138877.
192. F. Kang, X. Hu, J. Liu and Y. Gao, Noncovalent Binding of Polycyclic Aromatic Hydrocarbons with Genetic Bases Reducing the in Vitro Lateral Transfer of Antibiotic Resistant Genes, Environ. Sci. Technol., 2015, 49(17), 10340–10348,  DOI:10.1021/acs.est.5b02293.
193. P. B. Farmer, R. Singh, B. Kaur, R. J. Sram, B. Binkova, I. Kalina, T. A. Popov, S. Garte, E. Taioli, A. Gabelova and A. Cebulska-Wasilewska, Molecular Epidemiology Studies of Carcinogenic Environmental Pollutants, Mutat. Res., Rev. Mutat. Res., 2003, 544(2–3), 397–402,  DOI:10.1016/j.mrrev.2003.09.002.
194. X. Li, A. Z. Gu, Y. Zhang, B. Xie, D. Li and J. Chen, Sub-Lethal Concentrations of Heavy Metals Induce Antibiotic Resistance via Mutagenesis, J. Hazard. Mater., 2019, 369, 9–16,  DOI:10.1016/j.jhazmat.2019.02.006.
195. A. A. S. Sinegani and N. Younessi, Antibiotic Resistance of Bacteria Isolated from Heavy Metal-Polluted Soils with Different Land Uses, J. Global Antimicrob. Resist., 2017, 10, 247–255,  DOI:10.1016/j.jgar.2017.05.012.
196. J. Yuan, Y. Liu, J. Wang, Y. Zhao, K. Li, Y. Jing, X. Zhang, Q. Liu, X. Geng, G. Li and F. Wang, Long-Term Persistent Organic Pollutants Exposure Induced Telomere Dysfunction and Senescence-Associated Secretary Phenotype, J. Gerontol., Ser. A, 2018, 73(8), 1027–1035,  DOI:10.1093/gerona/gly002.
197. Y. Nahum, J. Muhvich, J. R. Morones-Ramirez, N. G. Casillas-Vega and M. H. Zaman, Biofilms as Potential Reservoirs of Antimicrobial Resistance in Vulnerable Settings, Front. Public Health, 2025, 13, 1568463,  DOI:10.3389/fpubh.2025.1568463.
198. B. D. Erath and A. R. Ferro, Infectious Disease Transmission from Bioaerosols, J. Exposure Sci. Environ. Epidemiol., 2022, 32(5), 645–646,  DOI:10.1038/s41370-022-00476-z.
199. J. R. Allison, S. Tiede, R. Holliday, J. Durham and N. S. Jakubovics, Bioaerosols and Airborne Transmission in the Dental Clinic, Int. Dent. J., 2024, 74, S418–S428,  DOI:10.1016/j.identj.2024.09.026.
200. N. Wéry, A. Galès and Y. Brunet, Bioaerosol Sources, in Microbiology of Aerosols, ed. A. Delort and P. Amato, Wiley, 2017, pp 115–135.  DOI:10.1002/9781119132318.ch2a.
201. M. Alsved, L. Bourouiba, C. Duchaine, J. Löndahl, L. C. Marr, S. T. Parker, A. J. Prussin and R. J. Thomas, Natural Sources and Experimental Generation of Bioaerosols: Challenges and Perspectives, Aerosol Sci. Technol., 2020, 54(5), 547–571,  DOI:10.1080/02786826.2019.1682509.
202. Y. S. Joung, Z. Ge and C. R. Buie, Bioaerosol Generation by Raindrops on Soil, Nat. Commun., 2017, 8(1), 14668,  DOI:10.1038/ncomms14668.
203. A. A. Laghari, L. Song, Y. Jia, A. Kumar, K. Rani, S. Gul, I. A. Jamro, S. Sajnani, J. Zhang, Y. Shen, A. Ali, M. Manzoor, Q. Guo and C. Wang, Bioaerosol Is an Important Source for Dissemination of Antibiotic Resistance Genes in Chicken Farms, Aerosol Sci. Technol., 2025, 59(6), 649–662,  DOI:10.1080/02786826.2025.2471867.
204. P. B. L. George, L. S. Hillary, S. Leclerc, E. C. Cooledge, J. Lemieux, C. Duchaine and D. L. Jones, Needles in Haystacks: Monitoring the Potential Escape of Bioaerosolised Antibacterial Resistance Genes from Wastewater Treatment Plants with Air and Phyllosphere Sampling, Can. J. Microbiol., 2024, 70(8), 348–357,  DOI:10.1139/cjm-2023-0226.
205. Y. Yang, R. Zhou, B. Chen, T. Zhang, L. Hu and S. Zou, Characterization of Airborne Antibiotic Resistance Genes from Typical Bioaerosol Emission Sources in the Urban Environment Using Metagenomic Approach, Chemosphere, 2018, 213, 463–471,  DOI:10.1016/j.chemosphere.2018.09.066.
206. K. Tang, Z. Huang, J. Huang, T. Maki, S. Zhang, A. Shimizu, X. Ma, J. Shi, J. Bi, T. Zhou, G. Wang and L. Zhang, Characterization of Atmospheric Bioaerosols along the Transport Pathway of Asian Dust during the Dust-Bioaerosol 2016 Campaign, Atmos. Chem. Phys., 2018, 18(10), 7131–7148,  DOI:10.5194/acp-18-7131-2018.
207. H. Mbareche, V. Dion-Dupont, M. Veillette, E. Brisebois, J. Lavoie and C. Duchaine, Influence of Seasons and Sites on Bioaerosols in Indoor Wastewater Treatment Plants and Proposal for Air Quality Indicators, J. Air Waste Manage. Assoc., 2022, 72(9), 1000–1011,  DOI:10.1080/10962247.2022.2066735.
208. A. T. Nair, Bioaerosols in the Landfill Environment: An Overview of Microbial Diversity and Potential Health Hazards, Aerobiologia, 2021, 37(2), 185–203,  DOI:10.1007/s10453-021-09693-9.
209. D. Wu, Y. Su, P. Wang, J. Zhao, J. Xie and B. Xie, Uncover Landfilled Antimicrobial Resistance: A Critical Review of Antibiotics Flux, Resistome Dynamics and Risk Assessment, Natl. Sci. Open, 2022, 1(2), 20220012,  DOI:10.1360/nso/20220012.
210. F. Rossi, R. Péguilhan, N. Turgeon, M. Veillette, J.-L. Baray, L. Deguillaume, P. Amato and C. Duchaine, Quantification of Antibiotic Resistance Genes (ARGs) in Clouds at a Mountain Site (Puy de Dôme, Central France), Sci. Total Environ., 2023, 865, 161264,  DOI:10.1016/j.scitotenv.2022.161264.
211. L. Fan, C. Chen, H. Zhang, Y. Zeng, T. Li, R. Gao, J. Li, Y. Ren, Z. Wu, F. Bi, Y. Chu, H. Li, J. Hu, J. Xu and Y. Xu, Atmospheric Detection, Prevalence, Transmission, Health and Ecological Consequences of Antibiotic Resistance Genes and Resistant Bacteria: A Comprehensive Review, Emerging Contam., 2025, 11(3), 100514,  DOI:10.1016/j.emcon.2025.100514.
212. B. Ghosh, H. Lal and A. Srivastava, Review of Bioaerosols in Indoor Environment with Special Reference to Sampling, Analysis and Control Mechanisms, Environ. Int., 2015, 85, 254–272,  DOI:10.1016/j.envint.2015.09.018.
213. E. Klvanova, P. Videnska, V. Barton, J. Bohm, P. Splichalova, V. Koksova, M. Urik, B. Lanickova, R. Prokes, E. Budinska, J. Klanova and P. Borilova Linhartova, Resistome, in the Indoor Dust Samples from Workplaces and Households: A Pilot Study, Front. Cell. Infect. Microbiol., 2024, 14, 1484100,  DOI:10.3389/fcimb.2024.1484100.
214. S. Cha, S. Srinivasan, J. H. Jang, D. Lee, S. Lim, K. S. Kim, W. Jheong, D.-W. Lee, E.-R. Park, H.-M. Chung, J. Choe, M. K. Kim and T. Seo, Metagenomic Analysis of Airborne Bacterial Community and Diversity in Seoul, Korea, during December 2014, Asian Dust Event, PLoS One, 2017, 12(1), e0170693,  DOI:10.1371/journal.pone.0170693.
215. A. W. D'Souza, M. Boolchandani, S. Patel, G. Galazzo, J. M. Van Hattem, M. S. Arcilla, D. C. Melles, M. D. De Jong, C. Schultsz, COMBAT Consortium, M. C. J. Bootsma, P. J. Van Genderen, A. Goorhuis, M. P. Grobusch, N. Molhoek, A. M. L. Oude Lashof, E. E. Stobberingh, H. A. Verbrugh, G. Dantas and J. Penders, Destination Shapes Antibiotic Resistance Gene Acquisitions, Abundance Increases, and Diversity Changes in Dutch Travelers, Genome Med., 2021, 13(1), 79,  DOI:10.1186/s13073-021-00893-z.
216. M. Sun, M. Ye, A. P. Schwab, X. Li, J. Wan, Z. Wei, J. Wu, V.-P. Friman, K. Liu, D. Tian, M. Liu, H. Li, F. Hu and X. Jiang, Human Migration Activities Drive the Fluctuation of ARGs: Case Study of Landfills in Nanjing, Eastern China, J. Hazard. Mater., 2016, 315, 93–101,  DOI:10.1016/j.jhazmat.2016.04.077.
217. C. A. Ahlstrom, M. L. Van Toor, H. Woksepp, J. C. Chandler, J. A. Reed, A. B. Reeves, J. Waldenström, A. B. Franklin, D. C. Douglas, J. Bonnedahl and A. M. Ramey, Evidence for Continental-Scale Dispersal of Antimicrobial Resistant Bacteria by Landfill-Foraging Gulls, Sci. Total Environ., 2021, 764, 144551,  DOI:10.1016/j.scitotenv.2020.144551.
218. S. J. Cooke, M. L. Piczak, N. J. Singh, S. Åkesson, A. T. Ford, S. Chowdhury, G. W. Mitchell, D. R. Norris, M. Hardesty-Moore, D. McCauley, N. Hammerschlag, M. A. Tucker, J. J. Horns, R. R. Reisinger, V. Kubelka and R. J. Lennox, Animal Migration in the Anthropocene: Threats and Mitigation Options, Biol. Rev., 2024, 99(4), 1242–1260,  DOI:10.1111/brv.13066.
219. T. Alerstam and J. Bäckman, Ecology of Animal Migration, Curr. Biol., 2018, 28(17), R968–R972,  DOI:10.1016/j.cub.2018.04.043.
220. J. D. Kotwa, B. Lobb, A. Massé, M. Gagnier, P. Aftanas, A. Banerjee, A. Banete, J. Blais-Savoie, J. Bowman, T. Buchanan, H.-Y. Chee, P. Kruczkiewicz, K. Nirmalarajah, C. Soos, O. Vernygora, L. Yip, L. R. Lindsay, A. J. McGeer, F. Maguire, O. Lung, A. C. Doxey, B. Pickering and S. Mubareka, Genomic and Transcriptomic Characterization of Delta SARS-CoV-2 Infection in Free-Ranging White-Tailed Deer (Odocoileus Virginianus), iScience, 2023, 26(11), 108319,  DOI:10.1016/j.isci.2023.108319.
221. V. L. Hale, P. M. Dennis, D. S. McBride, J. M. Nolting, C. Madden, D. Huey, M. Ehrlich, J. Grieser, J. Winston, D. Lombardi, S. Gibson, L. Saif, M. L. Killian, K. Lantz, R. M. Tell, M. Torchetti, S. Robbe-Austerman, M. I. Nelson, S. A. Faith and A. S. Bowman, SARS-CoV-2 Infection in Free-Ranging White-Tailed Deer, Nature, 2022, 602(7897), 481–486,  DOI:10.1038/s41586-021-04353-x.
222. S. Bombaywala, N. A. Dafale, V. Jha, A. Bajaj and H. J. Purohit, Study of Indiscriminate Distribution of Restrained Antimicrobial Resistome of Different Environmental Niches, Environ. Sci. Pollut. Res., 2021, 28(9), 10780–10790,  DOI:10.1007/s11356-020-11318-6.
223. B. Van Bavel, L. Berrang-Ford, K. Moon, F. Gudda, A. J. Thornton, R. F. S. Robinson and R. King, Intersections between Climate Change and Antimicrobial Resistance: A Systematic Scoping Review, Lancet Planet. Health, 2024, 8(12), e1118–e1128,  DOI:10.1016/S2542-5196(24)00273-0.
224. M. Y. Alhassan and A. A. Ahmad, Antimicrobial Resistance in a Changing Climate: A One Health Approach for Adaptation and Mitigation, Bull. Natl. Res. Cent., 2025, 49(1), 26,  DOI:10.1186/s42269-025-01318-2.
225. Y. Wang, W. Tang, J. Qiao and L. Song, Occurrence and Prevalence of Antibiotic Resistance in Landfill Leachate, Environ. Sci. Pollut. Res., 2015, 22(16), 12525–12533,  DOI:10.1007/s11356-015-4514-7.
226. B. P. Naveen, J. Sumalatha and R. K. Malik, A Study on Contamination of Ground and Surface Water Bodies by Leachate Leakage from a Landfill in Bangalore, India, Int. J. Geo-Eng., 2018, 9(1), 27,  DOI:10.1186/s40703-018-0095-x.
227. J. P. Burnham, Climate Change and Antibiotic Resistance: A Deadly Combination, Ther. Adv. Infect. Dis., 2021, 8, 2049936121991374,  DOI:10.1177/2049936121991374.
228. J. P. R. Furlan, F. P. Sellera, N. Lincopan, D. Debone, S. G. E. K. Miraglia and R. A. Tavella, Catastrophic Floods and Antimicrobial Resistance: Interconnected Threats with Wide-Ranging Impacts, One Health, 2024, 19, 100891,  DOI:10.1016/j.onehlt.2024.100891.
229. J. Hanefeld, M. Khan, G. Tomson and R. Smith, Trade Is Central to Achieving the Sustainable Development Goals: A Case Study of Antimicrobial Resistance, Br. Med. J., 2017, j3505,  DOI:10.1136/bmj.j3505.
230. A. Tiwari, V. Gomez-Alvarez, S. Siponen, A. Sarekoski, A.-M. Hokajärvi, A. Kauppinen, E. Torvinen, I. T. Miettinen and T. Pitkänen, Bacterial Genes Encoding Resistance Against Antibiotics and Metals in Well-Maintained Drinking Water Distribution Systems in Finland, Front. Microbiol., 2022, 12, 803094,  DOI:10.3389/fmicb.2021.803094.
231. C. Laurens, H. Jean-Pierre, P. Licznar-Fajardo, S. Hantova, S. Godreuil, O. Martinez and E. Jumas-Bilak, Transmission of IMI-2 Carbapenemase-Producing Enterobacteriaceae from River Water to Human, J. Global Antimicrob. Resist., 2018, 15, 88–92,  DOI:10.1016/j.jgar.2018.06.022.
232. W.-L. Jia, M. Zhang, F.-Z. Gao, H. Bai, L.-X. He, L.-Y. He, T. Liu, Y. Han and G.-G. Ying, Antibiotic Resistome in Landfill Leachate and Impact on Groundwater, Sci. Total Environ., 2024, 927, 171991,  DOI:10.1016/j.scitotenv.2024.171991.
233. X. Guan, Z. Guo, X. Wang, S. Xiang, T. Sun, R. Zhao, J. He and F. Liu, Transfer Route and Driving Forces of Antibiotic Resistance Genes from Reclaimed Water to Groundwater, Environ. Pollut., 2023, 330, 121800,  DOI:10.1016/j.envpol.2023.121800.
234. M. L. Farrell, A. Chueiri, M. Maguire, A. Kovářová, G. Miliotis, L. O'Connor, F. McDonagh, S. Duane, M. Cormican, G. Devane, A. Tuohy, N. DeLappe, F. De Bock, L. P. Burke and D. Morris, Longitudinal Carriage of Antimicrobial Resistant Enterobacterales in Healthy Individuals in Ireland - Assessing the Impact of Recreational Water Use on Duration of Carriage, Sci. Total Environ., 2023, 905, 167100,  DOI:10.1016/j.scitotenv.2023.167100.
235. Environment and Climate Change Canada, Groundwater Contamination; Government Report, https://www.canada.ca/en/environment-climate-change/services/water-overview/pollution-causes-effects/groundwater-contamination.html#sub1a (accessed 2025-05-26).
236. A. Bhangu, A. O. Ademuyiwa, M. L. Aguilera, P. Alexander, S. W. Al-Saqqa, G. Borda-Luque, A. Costas-Chavarri, T. M. Drake, F. Ntirenganya, J. E. Fitzgerald, S. J. Fergusson, J. Glasbey, J. A. Ingabire, L. Ismaïl, H. K. Salem, A. T. T. Kojo, M. C. Lapitan, R. Lilford, A. L. Mihaljevic, D. Morton, A. Z. Mutabazi, D. Nepogodiev, A. O. Adisa, R. Ots, F. Pata, T. Pinkney, T. Poškus, A. U. Qureshi, A. Ramos-De La Medina, S. Rayne, C. A. Shaw, S. Shu, R. Spence, N. Smart, S. Tabiri, E. M. Harrison, C. Khatri, M. Mohan, Z. Jaffry, A. Altamini, A. Kirby, K. Søreide, G. Recinos, J. Cornick, M. M. Modolo, D. Iyer, S. King, T. Arthur, S. N. Nahar, A. Waterman, M. Walsh, A. Agarwal, A. Zani, M. Firdouse, T. Rouse, Q. Liu, J. C. Correa, P. Talving, M. Worku, A. Arnaud, V. Kalles, B. Kumar, S. Kumar, R. Amandito, R. Quek, L. Ansaloni, A. Altibi, D. Venskutonis, J. Zilinskas, T. Poskus, J. Whitaker, V. Msosa, Y. Y. Tew, A. Farrugia, E. Borg, Z. Bentounsi, T. Gala, I. Al-Slaibi, H. Tahboub, O. H. Alser, D. Romani, S. Shu, P. Major, A. Mironescu, M. Bratu, A. Kourdouli, A. Ndajiwo, A. Altwijri, M. U. Alsaggaf, A. Gudal, A. F. Jubran, S. Seisay, B. Lieske, I. Ortega, J. Jeyakumar, K. J. Senanayake, O. Abdulbagi, Y. Cengiz, D. Raptis, Y. Altinel, C. Kong, E. Teasdale, G. Irwin, M. Stoddart, R. Kabariti, S. Suresh, K. Gash, R. Narayanan, M. Maimbo, B. Grizhja, S. Ymeri, G. Galiqi, R. Klappenbach, D. Antezana, A. E. Mendoza Beleño, C. Costa, B. Sanchez, S. Aviles, C. G. Fermani, R. Balmaceda, S. Villalobos, J. M. Carmona, D. Hamill, P. Deutschmann, S. Sandler, D. Cox, R. Nataraja, C. Sharpin, D. Ljuhar, D. Gray, M. Haines, D. Iyer, N. Niranjan, S. D'Amours, M. Ashtari, H. Franco, A. Rahman Mitul, S. Karim, N. F. Aman, M. M. Estee, U. Salma, J. Razzaque, T. Hamid Kanta, S. A. Tori, S. Alamin, S. Roy, S. Al Amin, R. Karim, M. Haque, A. Faruq, F. Iftekhar, M. O'Shea, G. Padmore, R. Jonnalagadda, A. Litvin, A. Filatau, D. Paulouski, M. Shubianok, T. Shachykava, D. Khokha, V. Khokha, F. Djivoh, F. Dossou, D. M. Seto, D. G. Gbessi, B. Noukpozounkou, Y. Imorou Souaibou, K. R. Keke, F. Hodonou, E. Y. S. Ahounou, T. Alihonou, M. Dénakpo, G. Ahlonsou, A. Ginbo Bedada, C. Nsengiyumva, S. Kwizera, V. Barendegere, P. Choi, S. Stock, L. Jamal, G. Azzie, S. Kushwaha, T.-L. Chen, C. Yip, I. Montes, F. Zapata, S. Sierra, M. I. Villegas Lanau, M. C. Mendoza Arango, I. Mendoza Restrepo, R. S. Restrepo Giraldo, E. Domini, R. Karlo, J. Mihanovic, M. Youssef, H. Elfeki, W. Thabet, A. Sanad, G. Tawfik, A. Zaki, N. Abdel-Hameed, M. Mostafa, M. F. W. Omar, A. Ghanem, E. Abdallah, A. Denewer, E. Emara, E. Rashad, A. Sakr, R. Elashry, S. Emile, T. Khafagy, S. Elhamouly, A. Elfarargy, A. Mamdouh Mohamed, G. Saied Nagy, A. Esam, E. Elwy, A. Hammad, S. Khallaf, E. Ibrahim, A. Said Badr, A. Moustafa, A. Eldosouky Mohammed, M. Elgheriany, E. Abdelmageed, E. A. Al Raouf, E. Samir Elbanby, M. Elmasry, M. Morsy Farahat, E. Yahya Mansor, E. Magdy Hegazy, E. Gamal, H. Gamal, H. Kandil, D. Maher Abdelrouf, M. Moaty, D. Gamal, N. El-Sagheer, M. Salah, S. Magdy, A. Salah, A. Essam, A. Ali, M. Badawy, S. Ahmed, M. Mohamed, A. Assal, M. Sleem, M. Ebidy, A. Abd-Elrazek, D. Zahran, N. Adam, M. Nazir, A. B. Hassanein, A. Ismail, A. Elsawy, R. Mamdouh, M. Mabrouk, L. A. M. Ahmed, M. Hassab Alnaby, E. Magdy, M. Abd-Elmawla, M. Fahim, B. Mowafy, M. Ibrahim Mahmoud, M. Allam, M. Alkelani, N. Halim El Gendy, M. Saad Aboul-Naga, R. Alaa El-Din, A. H. Elgendy, M. Ismail, M. Shalaby, A. Adel Elsharkawy, M. Elsayed Moghazy, K. Hesham Elbisomy, H. Abdel Gawad Shakshouk, M. F. Hamed, M. M. Ebidy, M. Abdelkader, M. Karkeet, H. Ahmed, I. Adel, M. E. Omar, M. Ibrahim, O. Ghoneim, O. Hesham, S. Gamal, K. Hilal, O. Arafa, S. Adel Awad, M. Salem, F. Abdellatif Elsherif, N. Elsabbagh, M. R. Aboelsoud, A. Hossam Eldin Fouad Rida, A. Hossameldin, E. Hany, Y. Hosny Asar, N. Anwar, M. Gadelkarim, S. Abdelhady, E. Mohamed Morshedy, R. Saad, N. Soliman, M. Salama, E. Ezzat, A. Mohamed, A. Ibrahim, A. Fergany, S. Mohammed, A. Reda, Y. Allam, H. A. Saad, A. Abdelfatah, A. M. Fathy, A. El-Sehily, E. Abdalmageed Kasem, A. T. A. Hassan, A. R. Mohammed, A. G. Saad, Y. Elfouly, N. Elfouly, A. Ibrahim, A. Hassaan, M. M. Mohammed, G. Elhoseny, M. Magdy, E. Abd Elkhalek, Y. Zakaria, T. Ezzat, A. Abo El Dahab, M. Kelany, S. Arafa, O. Mokhtar Mohamed Hassan, N. Mohamed Badwi, A. Saber Sleem, H. Ahmed, K. Abdelbadeai, M. Abozed Abdullah, M. A. A. Lokman, S. Bahar, A. Rady Abdelazeam, A. Adelshone, M. Bin Hasnan, A. Zulkifli, S. N. A. Kamarulzamil, A. Elhendawy, A. Latif, A. Bin Adnan, S. Shaharuddin, A. H. Haji Abdul Majid, M. Amreia, D. Al-Marakby, M. Salma, M. J. B. Ismail, E. R. Mohd Basir, C. D. Mohd Ali, A. Y. Ata, M. Nasr, A. Rezq, A. Sheta, S. Tariq, A. E. Sallam, A. K. Darwish, S. Elmihy, S. Elhadry, A. Farag, H. Hajeh, A. Abdelaal, A. Aglan, A. Zohair, M. Essam, O. Moussa, E. El-Gizawy, M. Samy, S. Ali, E. El Halawany, A. Ata, M. El Halawany, M. Nashat, S. Soliman, A. Elazab, M. Samy, M. A. Abdelaziz, K. Ibrahim, A. M. Ibrahim, A. Gado, U. Hantour, E. Alm Eldeen, M. R. Loaloa, A. Abouzaid, M. Ahmed Bahaa Eldin, E. Hashad, F. Sroor, D. Gamil, E. Mahmoud Abdulhakeem, M. Zakaria, F. Mohamed, M. Abubakr, E. Ali, H. Magdy, M. T. Ramadan, M. Abdelaty Mohamed, S. Mansour, H. Abdul Aziz Amin, A. Rabie Mohamed, M. Saami, N. Ahmed Reda Elsayed, A. Tarek, S. Mohy Eldeen Mahmoud, I. Magdy El Sayed, A. Reda, M. Yusuf Shawky, M. Mousa Salem, S. Alaa El-Din, N. Abdullah Soliman, M. Talaat, S. Alaael-Dein, A. Abd Elmoen Elhusseiny, N. Abdullah, M. Elshaar, A. Abdelfatah Ibraheem, H. Abdulaziz, M. Kamal Ismail, M. Hamdy Madkor, M. Abdelaty, S. Mahmoud Abdel-Kader, O. Mohamed Salah, M. Eldafrawy, A. Zaki Eldeeb, M. Mahmoud Eid, A. Attia, K. Salah El-Dien, A. Shwky, M. A. Badenjki, A. Soliman, S. Mahmoud Al Attar, F. Sayed, F. Abdel Sabour, M. G. Azizeldine, M. Shawqi, A. Hashim, A. Aamer, A. M. Abdelraouf, M. Abdelshakour, A. Ibrahim, B. Mahmoud, M. Ali Mahmoud, M. Qenawy, A. M. Rashed, A. Dahy, M. Sayed, A. W. Shamsedine, B. Mohamed, A. Hasan, M. M. Saad, K. Abdul Bassit, N. Khalid Abd El-Latif, N. Elzahed, A. El Kashash, N. M. Bekhet, S. Hafez, A. Gad, M. E. Maher, A. Abd El-Sameea, M. Hafez, A. Sabe, A. Ahmed, A. Shahine, K. Dawood, S. Gaafar, R. Husseiny, O. Aboelmagd, A. Soliman, N. Mesbah, H. Emadeldin, A. Al Meligy, A. H. Bekhet, D. Hasan, K. Alhady, A. K. Sabe, M. A. Elnajjar, M. Aboelella, W. Hamsho, I. Hassan, H. Saad, G. Abdelazim, H. Mahmoud, N. Wael, A. M. Kandil, A. Magdy, S. Said Elkholy, B. E. Adel, K. Dabbour, S. Elsherbiney, O. Mattar, A. Khaled AbdRabou, M. Y. M. Aly, A. Geuoshy, A. Elnagar, S. Ahmed, I. Abdelmotaleb, A. A. Saleh, H. Mohammed Bakry, M. Saeed, S. Mahmoud, B. A. Tawfik, S. A. Ismail, E. Zakaria, M. O. Gad, M. Salah Elhelbawy, M. Bassem, N. Maraie, N. Medhat Elhadary, N. Semeda, S. Rabie Mohamed, H. M. Bakry, A. Essam, D. Tarek, K. Ashour, A. Elhadad, A. Abdel-Aty, I. Rakha, S. Mamdouh Matter, R. Abdelhamed, O. Abdelkader, A. Hassaan, Y. Soliman, A. Mohamed, S. Ghanem, S. Amr Mohamed Farouk, E. M. Ibrahim, E. El-Taher, M. Mostafa, M. F. Mahrous Badr, R. Elsemelawy, A. El-Sawy, A. Bakr, A. A. R. Al Rafati, S. Saar, A. Reinsoo, N. Seyoum, T. Worku, A. Fitsum, M. Tolonen, A. Leppäniemi, V. Sallinen, B. Parmentier, M. Peycelon, S. Irtan, S. Dardenne, E. Robert, B. Maillot, E. Courboin, A. P. Arnaud, J. Hascoet, O. Abbo, A. Ait Kaci, T. Prudhomme, Q. Ballouhey, C. Grosos, L. Fourcade, T. Cecilia, C. Jean-Francois, F.-C. Helene, X. Delforge, E. Haraux, B. Dousset, R. Schiavone, S. Gaujoux, J.-B. Marret, A. Haffreingue, J. Rod, M. Renaux-Petel, J.-F. Lecompte, J. Bréaud, P. Gastaldi, C. Taieb, R. Claire, E. Anis, N. Bustangi, M. Lopez, A. Scalabre, M. G. Grella, A. Mariani, G. Podevin, F. Schmitt, E. Hervieux, A. Broch, C. Muller, D. Bandoh, F. Abantanga, M. Kyereh, H. Asumah, E. K. Appiah, P. Wondoh, A. Gyedu, C. Dally, K. Agbedinu, M. Amoah, A. Yifieyeh, K. Agbedinu, F. Owusu, M. Amoako-Boateng, M. Dayie, R. Hagan, S. Debrah, M. Ohene-Yeboah, J.-N. Clegg-Lampety, V. Etwire, J. Dakubo, S. Essoun, W. Bonney, H. Glover-Addy, S. Osei-Nketiah, J. Amoako, N. Adu-Aryee, W. Appeadu-Mensah, A. Bediako-Bowan, F. Dedey, M. Ekow, E. Akatibo, M. Yakubu, H. E. K. Kordorwu, K. Asare-Bediako, E. Tackie, K. Aaniana, E. Acquah, R. Opoku-Agyeman, A. Avoka, K. Kusi, K. Maison, F. E. Gyamfi, G. Naa Barnabas, S. Abdul-Latif, P. Taah Amoako, A. Davor, V. Dassah, E. Dagoe, P. Kwakyeafriyie, E. Akoto, E. Ackom, E. Mensah, E. T. Atkins, C. L. Coompson, N. Ivros, C. Ferousis, V. Kalles, C. Agalianos, I. Kyriazanos, C. Barkolias, A. Tselos, G. Tzikos, E. Voulgaris, D. Lytras, A. Bamicha, K. Psarianos, A. Stefanopoulos, I. Patoulias, D. Sfougaris, I. Valioulis, D. Balalis, D. Korkolis, D. K. Manatakis, G. Kyrou, G. Karabelias, I.-A. Papaskarlatos, K. Konstantina, N. Zampitis, S. Germanos, A. Papailia, T. Theodosopoulos, G. Gkiokas, M. Mitroudi, C. Panteli, T. Feidantsis, K. Farmakis, D. Kyziridis, O. Ioannidis, S. Parpoudi, G. Gemenetzis, S. Parasyris, C. Anthoulakis, N. Nikoloudis, M. Margaritis, M.-L. Aguilera-Arevalo, O. Coyoy-Gaitan, J. Rosales, L. Tale, R. Soley, E. Barrios, S. T. T. Rodriguez, C. Paz Galvez, D. Herrera Cruz, G. Sanchez Rosenberg, A. Matheu, D. M. Cohen, M. Paul, A. Charles, J. C. Y. Lam, M. H. A. Yeung, C. Y. J. Fok, K. H. G. Li, A. C.-H. Lai, Y. H. E. Cheung, H. Y. Wong, K. W. Leung, T. S. B. Lee, W. H. Lam, W. Dao, S. H. Kwok, T.-Y. K. Chan, Y. K. Ng, T. Mak, C. C. Foo, J. Yang, A. Bhatnagar, V. Upadhyaya, U. Muddebihal, W. Dar, K. Janardhan, N. Aruldas, F. J. Adella, A. S. Rulie, F. Iskandar, J. Setiawan, C. V. Evajelista, H. Natalie, A. Suyadi, R. Gunawan, H. Karismaningtyas, L. P. S. Mata, F. F. A. Andika, A. Hasanah, T. A. Widiastini, N. A. Purwaningsih, A. D. F. Mukin, D. F. Rahmah, H. D. Nurqistan, H. M. Arsyad, N. Adhitama, W. S. Jeo, N. Sutandi, A. Clarissa, P. A. Gultom, M. Billy, A. Haloho, N. Johanna, F. Lee, R. M. N. Radin Dorani, M. Glynn, M. Alherz, W. Goh, H. A. Shiwani, L. Sproule, K. C. Conlon, M. Bala, A. Kedar, L. Turati, F. Bianco, F. Steccanella, G. Gallo, M. Trompetto, G. Clerico, M. Papandrea, G. Sammarco, R. Sacco, A. Benevento, L. Giavarini, M. C. Giglio, L. Bucci, G. Pagano, V. Sollazzo, R. Peltrini, G. Luglio, A. Birindelli, S. Di Saverio, G. Tugnoli, M. A. Paludi, P. Mingrone, D. Pata, F. Selvaggi, L. Selvaggi, G. Pellino, N. Di Martino, G. Curletti, P. Aonzo, R. Galleano, S. Berti, E. Francone, S. Boni, L. Lorenzon, A. Lo Conte, G. Balducci, G. Confalonieri, G. Pesenti, L. Gavagna, G. Vasquez, S. Targa, S. Occhionorelli, D. Andreotti, G. Pata, A. Armellini, D. Chiesa, F. Aquilino, N. Chetta, A. Picciariello, M. Abdelkhalek, A. Belli, S. De Franciscis, A. Bigaran, A. Favero, S. M. Basso, P. Salusso, M. Perino, S. Mochet, D. Sasia, F. Riente, M. Migliore, D. Merlini, S. Basilicò, C. Corbellini, V. Lazzari, Y. Macchitella, L. Bonavina, D. Angelieri, D. Coletta, F. Falaschi, M. Catani, C. Reali, M. Malavenda, C. Del Basso, S. Ribaldi, M. Coletti, A. Natili, N. Depalma, I. Iannone, A. Antoniozzi, D. Rossi, D. Gui, G. Perrotta, M. Ripa, F. R. Giardino, M. Foco, E. Vicario, F. Coccolini, G. E. Nita, N. Leone, A. Bondurri, A. Maffioli, A. Simioni, D. De Boni, S. Pasquali, E. Goldin, E. Vendramin, E. Ciccioli, U. Tedeschi, L. Bortolasi, P. Violi, T. Campagnaro, S. Conci, G. Lazzari, C. Iacono, A. Gulielmi, S. Manfreda, A. Rinaldi, M. N. Ringressi, B. Brunoni, G. Salamone, M. Mangiapane, P. De Marco, A. La Brocca, R. Tutino, V. Silvestri, L. Licari, T. Fontana, N. Falco, G. Cocorullo, M. Shalaby, P. Sileri, C. Arcudi, I. Bsisu, K. Aljboor, L. Abusalem, A. Alnusairat, A. Qaissieh, E. Al-Dakka, A. Ababneh, O. Halhouli, T. Yusufali, H. Mohammed, J. Lando, R. Parker, W. Ndegwa, M. Jokubauskas, J. Gribauskaite, J. Kuliavas, A. Dulskas, N. E. Samalavicius, K. Jasaitis, A. Parseliunas, V. Nevieraite, M. Montrimaite, E. Slapelyte, E. Dainius, R. Riauka, Z. Dambrauskas, A. Subocius, L. Venclauskas, A. Gulbinas, S. Bradulskis, S. Kasputyte, D. Mikuckyte, M. Kiudelis, T. Jankus, S. Petrikenas, M. Pažuskis, Z. Urniežius, M. Vilčinskas, V. J. Banaitis, V. Gaižauskas, E. Grisin, P. Mazrimas, R. Rackauskas, M. Drungilas, K. Lagunavicius, V. Lipnickas, D. Majauskyté, V. Jotautas, T. Abaliksta, L. Uščinas, G. Simutis, A. Ladukas, D. Danys, E. Laugzemys, S. Mikalauskas, E. Zdanyte Sruogiene, P. Višinskas, R. Žilinskienė, D. Dragatas, A. Burmistrovas, Z. Tverskis, A. Vaicius, R. Mazelyte, A. Zadoroznas, N. Kaselis, G. Žiubrytė, F. C. F. P. Rahantasoa, L. H. Samison, F. Rasoaherinomenjanahary, T. E. C. Tolotra, C. Mukuzunga, C. Kwatiwani, N. Msiska, F. Y. Chai, S. M. D. Asilah, K. Z. Syibrah, P. X. Chin, A. Salleh, N. Z. Riswan, A. C. Roslani, H.-Y. Chong, N. A. Aziz, K.-S. Poh, C.-A. Chai, S. Kumar, M. M. Taher, N. R. Kosai, D. N. Abdul Aziz, R. Rajan, R. Julaihi, D. L. Jethwani, M. T. Yahaya, N. A. Nik Abdullah, S. W. Mathew, K. J. Chung, M. K. Nirumal, R. G. Ern Tze, S. A. W. E. Wan Ali, Y. Y. Gan, J. R. S. Ting, S. S. Y. Sii, K. L. Koay, Y. K. Tan, A. E. Z. Cheah, C. Y. Wong, T. N. Tuan Mat, C. Y. N. Chow, P. A. Har, Y. Der, F. Henry, X. Low, Y. T. Neo, H. E. Heng, S. N. Kong, C. Gan, Y. T. Mok, Y. W. Tan, K. Palayan, M. Deva Tata, Y. J. Cheong, K. Gunaseelan, W. N. A. Wan Mohd Nasir, P. Yoganathan, E. X. Lee, J. E. Saw, L. J. Yeang, P. Y. Koh, S. Y. Lim, S. Y. Teo, N. Grech, D. Magri, K. Cassar, C. Mizzi, M. Falzon, N. Shaikh, R. Scicluna, S. Zammit, S. Mizzi, S. D. Brincat, T. Tembo, V. T. Hien Le, T. Grima, K. Sammut, K. Carabott, C. Zarb, A. Navarro, T. Dimech, G. M. Camilleri, I. Bertuello, J. Dalli, K. Bonavia, S. Corro-Diaz, M. Manriquez-Reyes, A. Abdelhamid, A. Hrora, S. Benammi, H. Bachri, M. Abbouch, K. Boukhal, R. M. Bennai, A. Belkouchi, M. S. Jabal, C. Benyaiche, M. Vermaas, L. Duinhouwer, J. Pastora, G. Wood, M. S. Merlo, A. Ajao, O. Ayandipo, T. Lawal, A. Abdurrazzaaq, M. Alada, A. Nasir, J. Adeniran, O. Habeeb, A. Popoola, A. Adeyeye, A. Adebanjo, O. Adesanya, A. Adeniyi, H. Mendel, B. Bello, U. Muktar, A. Osinowo, T. O. Olajide, O. Oshati, G. Ihediwa, B. Adenekan, V. Nwinee, F. Alakaloko, O. Elebute, A. Lawal, C. Bode, M. Olugbemi, A. Adesina, O. Faturoti, O. Odutola, O. Adebola, C. Onuoha, O. Taiwo, O. Williams, F. Balogun, O. Ajai, M. Oludara, I. Njokanma, R. Osuoji, S. Kache, J. Ajah, J. Makama, A. Adamu, S. Baba, M. Aliyu, S. Aliyu, Y. Ukwenya, H. Aliyu, T. Sholadoye, M. Daniyan, O. Ogunsua, L.-J. Anyanwu, A. Sheshe, A. Mohammad, S. Olori, P. Mshelbwala, B. Odeyemi, G. Samson, O. Kehinde Timothy, S. Ali Samuel, A. Ajiboye, I. Amole, O. Abiola, A. Olaolorun, T. Veen, A. Kanani, K. Styles, R. Herikstad, J. Wiik Larsen, J. A. Søreide, E. Jensen, M. Gran, E. K. Aahlin, T. Gaarder, P. W. Monrad-Hansen, P. A. Næss, G. Lauzikas, J. Wiborg, S. Holte, K. M. Augestad, G. S. Banipal, M. Monteleone, T. T. Moe, J. K. Schultz, N. Nadeem, M. Saqlain, J. Abbasy, A. R. Alvi, N. Shahzad, K. F. Bhopal, Z. Iftikhar, M. T. Butt, S. A. Ul Razi, A. Ahmed, A. Khan Niazi, I. Raza, F. Baluch, A. Raza, A. Bani-Sadar, M. Adil, A. Raza, M. Javaid, M. Waqar, M. A. Khan, M. M. Arshad, M. A. Amjad, T. Al-taher, A. Hamdan, A. Salman, R. Saadeh, A. Musleh, D. Jaradat, S. Abushamleh, S. Hanoun, A. Abu Qumbos, A. Hamarshi, A. A. Taher, I. Qawasmi, K. Qurie, M. Altarayra, M. Ghannam, A. Shaheen, A. Herebat, A. Abdelhaq, A. Shalabi, M. Abu-toyour, F. Asi, A. Shamasneh, A. Atiyeh, M. Mustafa, R. Zaa'treh, M. Dabboor, E. Alaloul, H. Baraka, J. Meqbil, A. Al-Buhaisi, M. Elshami, S. Afana, S. Jaber, S. Alyacoubi, Y. Abuowda, T. Idress, E. Abuqwaider, S. Al-saqqa, A. Bowabsak, A. El Jamassi, D. Hasanain, H. Al-Farram, M. Salah, A. Firwana, M. Hamdan, I. Awad, A. Ashour, F. E. Al Barrawi, A. Alkhatib, M. Al-Faqawi, M. Fares, A. Elmashala, M. Adawi, I. Adawi, R. Khreishi, R. Khreishi, A. Ashour, A. Ghaben, G. M. Machain Vega, J. T. Cardozo, M. O. Roche, G. R. Pertersen Servin, H. A. Segovia Lohse, L. I. Páez Lopez, R. A. M. Cardozo, F. Espinoza, A. D. Pérez Rojas, D. Sanchez, C. S. Samaniego, S. Guevara Torres, A. C. Calua, C. Razuri, N. Ortiz, X. Rodriguez, N. Carrasco, F. Saravia, H. Shibao Miyasato, M. Valcarcel-Saldaña, Y. E. A. Bermúdez, J. Carpio, W. Ruiz Panez, P. A. Toribio Orbegozo, C. Guzmán Dueñas, K. Turpo Espinoza, A. M. Sandoval Barrantes, J. A. Chungui Bravo, L. Fuentes-Rivera, C. Fernández, B. Málaga, J. Ye, R. Velasquez, J. Salcedo, A. L. Contreras-Vergara, A. G. Vergara Mejia, M. S. Gonzales Montejo, M. D. C. Escalante Salas, W. Alcca Ticona, M. Vargas, G. C. Manrique Sila, R. Mas, A. Del Pilar Paucar, A. J. Román Velásquez, A. Robledo-Rabanal, L. A. Z. Solis, K. Turpo Espinoza, J. L. Hamasaki Hamaguchi, E. S. Florez Farfan, L. A. Madrid Barrientos, J. J. Herrera Matta, J. J. V. Mora, M. A. P. Redota, M. F. Roxas, M. J. B. Maño, M. D. Parreno-Sacdalan, C. L. Almanon, M. Walędziak, R. Roszkowski, M. Janik, A. Lasek, D. Radkowiak, M. Rubinkiewicz, C. Fernandes, J. Costa-Maia, R. Melo, L. Muntean, A. S. Mironescu, L. C. Vida, M. Popa, H. Mircea, M. Vartic, B. Diaconescu, M. R. Bratu, I. Negoi, M. Beuran, C. Ciubotaru, N. Uzabumwana, D. Duhoranenayo, E. Jovine, N. Zanini, G. Landolfo, M. Aljiffry, F. Idris, M. S. A. Alghamdi, A. Maghrabi, A. Altaf, A. Alkaaki, A. Khoja, A. Nawawi, S. Turkustani, E. Khalifah, A. Albiety, S. Sahel, R. Alshareef, M. Najjar, A. Alzahrani, A. Alghamdi, W. Alhazmi, G. Al Saied, M. Alamoudi, M. M. Riaz, M. Hassanain, B. Alhassan, A. Altamimi, R. Alyahya, N. Al Subaie, F. Al Bastawis, A. Altamimi, T. Nouh, R. Khan, M. Radojkovic, L. Jeremic, M. Nestorovic, J. H. Law, K. S. K. Tan, R. C. K. Tan, J. K. Tan, L. W. L. Joel, X. W. Chan, F. Q. H. Leong, C. S. Chong, S. Koh, K. Y. Lee, K. C. Lee, K. Pluke, B. Dedekind, P. Nashidengo, M. I. Hampton, J. Joosten, S. Sobnach, L. Roodt, A. Sander, J. Pape, N. Maistry, P. Ndwambi, K. Kinandu, M. Tun, F. Du Toit, Q. Ellison, S. Burger, D. Grobler, L. B. Khulu, R. Moore, V. Jennings, A. Leusink, N. Kariem, J. Gouws, K. Chu, H. Bougard, F. Noor, A. Dell, S. Van Straten, A. Khamajeet, S. K. Tshisola, K. Kabongo, V. Kong, Y. Moodley, F. Anderson, T. Madiba, F. Du Plooy, L. Hartford, G. Chilton, P. Karjiker, M. E. Mabitsela, S. R. Ndlovu, M. Badicel, R. Jaich, J. Ruiz-Tovar, L. Garcia-Florez, J. L. Otero-Díez, V. Ramos Pérez, N. Aguado Suárez, J. Minguez García, S. Corral Moreno, M. V. Collado, V. Jiménez Carneros, J. García Septiem, M. Gonzalez, A. Picardo, E. Esteban, E. Ferrero, E. Espin-Basany, R. Blanco-Colino, V. Andriola, L. Solar García, E. Contreras, C. García Bernardo, J. Pagnozzi, S. Sanz, A. Miyar De León, A. Dorismé, J. Rodicio, A. Suarez, J. Stuva, T. Diaz Vico, L. Fernandez-Vega, C. Soldevila-Verdeguer, F. Sena-Ruiz, N. Pujol-Cano, P. Diaz-Jover, J. M. Garcia-Perez, J. J. Segura-Sampedro, C. Pineño-Flores, D. Ambrona-Zafra, A. Craus-Miguel, P. Jimenez-Morillas, A. Mazzella, A. Jayathilake, S. Thalgaspitiya, L. Wijayarathna, P. Wimalge, H. A. Sanni, O. Okenabirhie, A. Homeida, A. Younis, O. A. Omer, M. Abdulaziz, A. Mussad, A. Adam, I. Björklund, S. Ahlqvist, A. Thorell, F. Wogensen, A. Sokratous, M. Breistrand, H. Thorarinsdottir, J. Sigurdadottir, M. Nikberg, A. Chabok, M. Hjertberg, P. Elbe, D. Saraste, W. Rutkowski, L. Forlin, K. Niska, M. Sund, D. Oswald, G. Peros, R. Bluelle, K. Reinisch, D. Frey, A. Palma, D. A. Raptis, L. Zumbühl, M. Zuber, R. Schmid, G. Werder, A. Nocito, A. Gerosa, S. Mahanty, L. W. Widmer, J. Müller, A. Gübeli, G. Zuk, O. B. Gulcicek, T. Vartanoglu, E. Kose, S. R. Karahan, M. C. Aydin, N. A. Sahbaz, I. Halicioglu, H. Alis, I. Sapci, C. Adiyaman, A. M. Pektaş, T. B. Cengiz, I. Tansoker, V. Işler, M. Cevik, D. Mutlu, V. Ozben, B. B. Ozmen, S. Bayram, S. Yolcu, B. B. Kobal, ÖF Toto, H. C. Çakaloğlu, K. Karabulut, V. Mutlu, B. B. Ozkan, S. Celik, A. Semiz, S. Bodur, E. Gül, B. Murutoglu, R. Yildirim, B. E. Baki, E. Arslan, M. Ulusahin, A. Guner, K. Tomas, N. Walker, N. Shrimanker, S. Cole, R. Breslin, R. Srinivasan, M. Elshaer, K. Hunter, A. Al-Bahrani, I. Liew, N. G. Mairs, A. Rocke, L. Dick, M. Qureshi, D. Chowdhury, N. Wright, C. Skerritt, D. Kufeji, A. Ho, T. Dissanayake, A. Tennakoon, W. Ali, S. J. Lim, C. Tan, S. O'Neill, C. Jones, S. Knight, D. Nassif, A. Sharma, O. Warren, R. White, A. Mehdi, N. Post, E. Kalakouti, E. Dashnyam, F. Stourton, I. Mykoniatis, C. Currow, F. Wong, A. Gupta, V. Shatkar, J. Luck, S. Kadiwar, A. Smedley, R. Wakefield, P. Herrod, J. Blackwell, J. Lund, F. Cohen, A. Bandi, S. Giuliani, G. Bond-Smith, T. Pezas, N. Farhangmehr, T. Urbonas, M. Perenyei, P. Ireland, N. Blencowe, K. Bowling, D. Bunting, L. Longstaff, K. Keogh, H. Jeon, M. R. Iqbal, S. Khosla, A. Jeffery, J. Perera, A. A. Ibrahem, T. Alhammali, Y. Salama, S. Oram, T. Kidd, F. Cullen, C. Owen, M. Wilson, S. Chiu, H. Sarafilovic, J. Ploski, E. Evans, A. Abbas, S. Kamya, N. Ishak, C. Bisset, C. Andress, Y. R. Chin, P. Patel, D. Evans, A. Haslegrave, A. Boggon, K. Laurie, K. Connor, T. Mann, A. Mansuri, R. Davies, E. Griffiths, A. R. Shahbaz, C. Eng, F. Din, A. L'Heveder, E. H. Park, R. Ravishankar, K. McIntosh, J. D. Yau, L. Chan, S. McGarvie, L. Tang, H. Lim, S. Yap, J. Park, Z. H. Ng, S. Mirza, Y. L. Ang, L. Walls, C. Roy, S. Paterson-Brown, J. Camilleri-Brennan, K. Mclean, M. S. D'Souza, S. Pronin, D. E. Henshall, E. Z. Ter, D. Fouad, A. Minocha, W. English, C. Morgan, D. Townsend, L. Maciejec, S. Mahdi, O. Akpenyi, E. Hall, H. Caydiid, Z. Rob, T. Abbott, H. D. Torrance, R. Johnston, M. A. Gani, G. Gravante, S. Rajmohan, K. Majid, S. Dindyal, C. Smith, M. Palliyil, S. Patel, L. Nicholson, N. Harvey, K. Baillie, S. Shillito, S. Kershaw, R. Bamford, P. Orton, E. Reunis, R. Tyler, W. C. Soon, G. M. Jama, D. Dhillon, K. Patel, S. Nanthakumaran, R. Heard, K. Y. Chen, B. Barmayehvar, U. Datta, S. K. Kamarajah, S. Karandikar, S. Iftekhar Tani, E. Monaghan, P. Donnelly, M. Walker, J. Parakh, S. Blacker, A. Kaul, A. Paramasivan, S. Farag, A. Nessa, S. Awadallah, J. Lim, J. Chean Khun Ng, R. P. Kiran, A. Murray, E. Etchill, M. Dasari, J. Puyana, N. Haddad, M. Zielinski, A. Choudhry, C. Caliman, M. Beamon, T. Duane, M. Swaroop, J. Myers, R. Deal, E. Schadde, M. Hemmila, L. Napolitano, K. To, A. Makupe, J. Musowoya, N. Van Der Naald, D. Kumwenda, A. Reece-Smith, K. Otten, A. Verbeek, M. Prins, A. A. Baquero Suarez, R. Balmaceda, C. Deane, E. Dijan, M. Elfiky, L. Koskenvuo, A. Thollot, B. Limoges, C. Capito, C. Alexandre, H. Kotobi, J. Leroux, K. Pinnagoda, N. Henric, O. Azzis, O. Rosello, P. Francois, S. Etienne, P. Buisson, S. Hmila, J.-N. Clegg-Lamptey, O. Imoro, O. E. Abem, D. Papageorgiou, V. Soulou, S. Asturias, L. Peña, D. B. O'Connor, A. R. Luc, A. A. Russo, A. Ruzzenente, A. Taddei, C. Cona, C. Bottini, G. Pascale, G. Rotunno, L. Solaini, M. M. Pascale, M. Notarnicola, M. Corbellino, M. Sacco, P. Ubiali, R. Cautiero, T. Bocchetti, E. Muzio, V. Guglielmo, E. Morandi, P. Mao, E. De Luca, F. M. Ali, J. Žilinskas, K. Strupas, P. Kondrotas, R. Baltrunas, J. Kutkevicius, P. Ignatavicius, C. L. Tan, J. Y. Siaw, S. Y. Yam, L. Wilson, M. R. A. Aziz, J. Bondin, C. D. Zorrilla, A. Majbar, D. Sale, L. Abdullahi, O. Osagie, O. Faboya, A. Fatuga, A. Taiwo, E. Nwabuoku, M. Bliksøen, Z. A. Khan, J. Coronel, C. Miranda, I. Vasquez, L. M. Helguero-Santin, J. Rickard, A. Adedeji, S. Alqahtani, M. Rath, M. Van Niekerk, M. Z. Koto, R. Matos-Puig, L. Israelsson, T. Schuetz, M. A. Yuksek, M. Mericliler, M. Ulusahin, B. Wolf, C. Fairfield, G. L. Yong, K. Whitehurst, N. Redgrave, C. K. Musyoka, J. Olivier, K. Lee, M. Cox, M. M. H. Farhan-Alanie, R. Callan, C. Chibuye, T. H. A. Ali, S. Rekhis, M. Rommaneh, Z. H. Sam, T. B. Pugliesi, G. Pardo and R. Blanco, Surgical Site Infection after Gastrointestinal Surgery in High-Income, Middle-Income, and Low-Income Countries: A Prospective, International, Multicentre Cohort Study, Lancet Infect. Dis., 2018, 18(5), 516–525,  DOI:10.1016/S1473-3099(18)30101-4.
237. L. Andrade, M. Kelly, P. Hynds, J. Weatherill, A. Majury and J. O'Dwyer, Groundwater Resources as a Global Reservoir for Antimicrobial-Resistant Bacteria, Water Res., 2020, 170, 115360,  DOI:10.1016/j.watres.2019.115360.
238. M. Alawi, C. Smyth, D. Drissner, A. Zimmerer, D. Leupold, D. Müller, T. T. Do, T. Velasco-Torrijos and F. Walsh, Private and Well Drinking Water Are Reservoirs for Antimicrobial Resistant Bacteria, npj Antimicrob. Resist., 2024, 2(1), 7,  DOI:10.1038/s44259-024-00024-9.
239. J. Dietvorst, L. Vilaplana, N. Uria, M.-P. Marco and X. Muñoz-Berbel, Current and Near-Future Technologies for Antibiotic Susceptibility Testing and Resistant Bacteria Detection, TrAC, Trends Anal. Chem., 2020, 127, 115891,  DOI:10.1016/j.trac.2020.115891.
240. V. C. Dias, V. L. da Silva, R. Barros, A. N. Bastos, L. Q. de Andrade Bastos, V. Q. de Andrade Bastos and C. G. Diniz, Phenotypic and Genotypic Evaluation of Beta-Lactamases (ESBL and KPC) among Enterobacteria Isolated from Community-Acquired Monomicrobial Urinary Tract Infections, J. Chemother., 2014, 26(6), 328–332,  DOI:10.1179/1973947813Y.0000000148.
241. R. Vanstokstraeten, D. Piérard, F. Crombé, D. De Geyter, I. Wybo, A. Muyldermans, L. Seyler, B. Caljon, T. Janssen and T. Demuyser, Genotypic Resistance Determined by Whole Genome Sequencing versus Phenotypic Resistance in 234 Escherichia Coli Isolates, Sci. Rep., 2023, 13(1), 449,  DOI:10.1038/s41598-023-27723-z.
242. R. Barlow, K. Mcmillan, G. Mellor, L. Duffy, D. Jordan, R. Abraham, M. O′dea, S. Sahibzada and S. Abraham, Phenotypic and Genotypic Assessment of Antimicrobial Resistance in Escherichia Coli from Australian Cattle Populations at Slaughter, J. Food Prot., 2022, 85(4), 563–570,  DOI:10.4315/JFP-21-430.
243. F. Coll, T. Gouliouris, B. Blane, C. A. Yeats, K. E. Raven, C. Ludden, F. A. Khokhar, H. J. Wilson, L. W. Roberts, E. M. Harrison, C. S. Horner, T. H. Le, T. H. Nguyen, V. T. Nguyen, N. M. Brown, M. A. Holmes, J. Parkhill, M. E. Török and S. J. Peacock, Antibiotic Resistance Determination Using Enterococcus Faecium Whole-Genome Sequences: A Diagnostic Accuracy Study Using Genotypic and Phenotypic Data, Lancet. Microbe, 2024, 5(2), e151–e163,  DOI:10.1016/S2666-5247(23)00297-5.
244. H. Wang, C. Jia, H. Li, R. Yin, J. Chen, Y. Li and M. Yue, Paving the Way for Precise Diagnostics of Antimicrobial Resistant Bacteria, Front. Mol. Biosci., 2022, 9, 976705,  DOI:10.3389/fmolb.2022.976705.
245. J. Hudzicki, Kirby-Bauer Disk Diffusion Susceptibility Test Protocol, Am. Soc. Microbiol., 2009, 15, 55–63.
246. J. J. Biemer, Antimicrobial Susceptibility Testing by the Kirby-Bauer Disc Diffusion Method, Ann. Clin. Lab. Sci., 1973, 3(2), 135–140.
247. M. B. Huang, C. N. Baker, S. Banerjee and F. C. Tenover, Accuracy of the E Test for Determining Antimicrobial Susceptibilities of Staphylococci, Enterococci, Campylobacter Jejuni, and Gram-Negative Bacteria Resistant to Antimicrobial Agents, J. Clin. Microbiol., 1992, 30(12), 3243–3248,  DOI:10.1128/jcm.30.12.3243-3248.1992.
248. R. Fattouh, N. Tijet, A. McGeer, S. M. Poutanen, R. G. Melano and S. N. Patel, What Is the Appropriate Meropenem MIC for Screening of Carbapenemase-Producing Enterobacteriaceae in Low-Prevalence Settings?, Antimicrob. Agents Chemother., 2016, 60(3), 1556–1559,  DOI:10.1128/AAC.02304-15.
249. M. Nguyen, R. Olson, M. Shukla, M. VanOeffelen and J. J. Davis, Predicting Antimicrobial Resistance Using Conserved Genes, PLoS Comput. Biol., 2020, 16(10), e1008319,  DOI:10.1371/journal.pcbi.1008319.
250. S. Arya, A. Williams, S. V. Reina, C. W. Knapp, J.-U. Kreft, J. L. Hobman and D. J. Stekel, Towards a General Model for Predicting Minimal Metal Concentrations Co-Selecting for Antibiotic Resistance Plasmids, Environ. Pollut., 2021, 275, 116602,  DOI:10.1016/j.envpol.2021.116602.
251. Y. Xu, Y. Mao, X. Hua, Y. Jiang, Y. Zou, Z. Wang, Z. Liu, H. Zhang, L. Lu and Y. Yu, Machine Learning-Based Prediction of Antimicrobial Resistance and Identification of AMR-Related SNPs in Mycobacterium Tuberculosis, BMC Genom. Data, 2025, 26(1), 48,  DOI:10.1186/s12863-025-01338-x.
252. K. Hu, F. Meyer, Z.-L. Deng, E. Asgari, T.-H. Kuo, P. C. Münch and A. C. McHardy, Assessing Computational Predictions of Antimicrobial Resistance Phenotypes from Microbial Genomes, Briefings Bioinf., 2024, 25(3), bbae206,  DOI:10.1093/bib/bbae206.
253. K. P. Smith and J. E. Kirby, Rapid Susceptibility Testing Methods, Clin. Lab. Med., 2019, 39(3), 333–344,  DOI:10.1016/j.cll.2019.04.001.
254. G. D. Kaprou, I. Bergšpica, E. A. Alexa, A. Alvarez-Ordóñez and M. Prieto, Rapid Methods for Antimicrobial Resistance Diagnostics, Antibiotics, 2021, 10(2), 209,  DOI:10.3390/antibiotics10020209.
255. D. Yamin, V. Uskoković, A. Wakil, M. Goni, S. Shamsuddin, F. Mustafa, W. Alfouzan, M. Alissa, A. Alshengeti, R. Almaghrabi, M. Fares, M. Garout, N. Al Kaabi, A. Alshehri, H. Ali, A. Rabaan, F. Aldubisi, C. Yean and N. Yusof, Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria, Diagnostics, 2023, 13(20), 3246,  DOI:10.3390/diagnostics13203246.
256. I. Gajic, J. Kabic, D. Kekic, M. Jovicevic, M. Milenkovic, D. Mitić-Ćulafić, A. Trudic, L. Ranin and N. Opavski, Antimicrobial Susceptibility Testing: A Comprehensive Review of Currently Used Methods, Antibiotics, 2022, 11(4), 427,  DOI:10.3390/antibiotics11040427.
257. D. Fraisl, G. Hager, B. Bedessem, M. Gold, P.-Y. Hsing, F. Danielsen, C. B. Hitchcock, J. M. Hulbert, J. Piera, H. Spiers, M. Thiel and M. Haklay, Citizen Science in Environmental and Ecological Sciences, Nat. Rev. Methods Primer, 2022, 2(1), 64,  DOI:10.1038/s43586-022-00144-4.
258. C. B. Cooper, C. L. Hawn, L. R. Larson, J. K. Parrish, G. Bowser, D. Cavalier, R. R. Dunn, M. Haklay, K. K. (Muki) Gupta, N. O. Jelks, V. A. Johnson, M. Katti, Z. Leggett, O. R. Wilson and S. Wilson, Inclusion in Citizen Science: The Conundrum of Rebranding, Science, 2021, 372(6549), 1386–1388,  DOI:10.1126/science.abi6487.
259. J. Von Gönner, T. Masson, S. Köhler, I. Fritsche and A. Bonn, Citizen Science Promotes Knowledge, Skills and Collective Action to Monitor and Protect Freshwater Streams, People Nat., 2024, 6(6), 2357–2373,  DOI:10.1002/pan3.10714.
260. Z. Yu, P. He, L. Shao, H. Zhang and F. Lü, Co-Occurrence of Mobile Genetic Elements and Antibiotic Resistance Genes in Municipal Solid Waste Landfill Leachates: A Preliminary Insight into the Role of Landfill Age, Water Res., 2016, 106, 583–592,  DOI:10.1016/j.watres.2016.10.042.
261. M. Laforest, K. Bisaillon, M. Ciotola, M. Cadieux, P.-O. Hébert, V. Toussaint and A. M. Svircev, Rapid Identification of Erwinia Amylovora and Pseudomonas Syringae Species and Characterization of E. Amylovora Streptomycin Resistance Using Quantitative PCR Assays, Can. J. Microbiol., 2019, 65(7), 496–509,  DOI:10.1139/cjm-2018-0587.
262. X.-L. An, J.-Q. Su, B. Li, W.-Y. Ouyang, Y. Zhao, Q.-L. Chen, L. Cui, H. Chen, M. R. Gillings, T. Zhang and Y.-G. Zhu, Tracking Antibiotic Resistome during Wastewater Treatment Using High Throughput Quantitative PCR, Environ. Int., 2018, 117, 146–153,  DOI:10.1016/j.envint.2018.05.011.
263. H. Sanderson, R. Ortega-Polo, K. McDermott, G. Hall, R. Zaheer, R. S. Brown, A. Majury, T. A. McAllister and S. N. Liss, Quantification and Multidrug Resistance Profiles of Vancomycin-Resistant Enterococci Isolated from Two Wastewater Treatment Plants in the Same Municipality, Microorganisms, 2019, 7(12), 626,  DOI:10.3390/microorganisms7120626.
264. F. M. Liotti, B. Posteraro, F. Mannu, F. Carta, A. Pantaleo, G. De Angelis, G. Menchinelli, T. Spanu, P. L. Fiori, F. Turrini and M. Sanguinetti, Development of a Multiplex PCR Platform for the Rapid Detection of Bacteria, Antibiotic Resistance, and Candida in Human Blood Samples, Front. Cell. Infect. Microbiol., 2019, 9, 389,  DOI:10.3389/fcimb.2019.00389.
265. T. T. N. Dung, V. V. Phat, C. Vinh, N. P. H. Lan, N. L. N. Phuong, L. T. Q. Ngan, G. Thwaites, L. Thwaites, M. Rabaa, A. T. K. Nguyen and P. T. Duy, Development and Validation of Multiplex Real-Time PCR for Simultaneous Detection of Six Bacterial Pathogens Causing Lower Respiratory Tract Infections and Antimicrobial Resistance Genes, BMC Infect. Dis., 2024, 24(1), 164,  DOI:10.1186/s12879-024-09028-2.
266. B. Hu, Y. Tao, Z. Shao, Y. Zheng, R. Zhang, X. Yang, J. Liu, X. Li and R. Sun, A Comparison of Blood Pathogen Detection Among Droplet Digital PCR, Metagenomic Next-Generation Sequencing, and Blood Culture in Critically Ill Patients With Suspected Bloodstream Infections, Front. Microbiol., 2021, 12, 641202,  DOI:10.3389/fmicb.2021.641202.
267. T. Erler, F. Droop, C. Lübbert, J. K. Knobloch, L. Carlsen, C. Papan, T. Schwanz, J. Zweigner, J. Dengler, M. Hoffmann, N. T. Mutters and M. Savin, Analysing Carbapenemases in Hospital Wastewater: Insights from Intracellular and Extracellular DNA Using qPCR and Digital PCR, Sci. Total Environ., 2024, 950, 175344,  DOI:10.1016/j.scitotenv.2024.175344.
268. J. Wu, B. Tang, Y. Qiu, R. Tan, J. Liu, J. Xia, J. Zhang, J. Huang, J. Qu, J. Sun, X. Wang and H. Qu, Clinical Validation of a Multiplex Droplet Digital PCR for Diagnosing Suspected Bloodstream Infections in ICU Practice: A Promising Diagnostic Tool, Crit. Care, 2022, 26(1), 243,  DOI:10.1186/s13054-022-04116-8.
269. S. Coyne, G. Guigon, P. Courvalin and B. Périchon, Screening and Quantification of the Expression of Antibiotic Resistance Genes in Acinetobacter Baumannii with a Microarray, Antimicrob. Agents Chemother., 2010, 54(1), 333–340,  DOI:10.1128/AAC.01037-09.
270. P. M. de Macedo, A. Sturny-Leclère, S. Hamane, T. Pautet, A. M. Rodrigues, D. F. S. Freitas, A. C. F. D. Valle, R. M. Zancopé-Oliveira, R. Almeida-Paes and A. Alanio, A New Quantitative Reverse Transcription PCR Assay to Improve the Routine Diagnosis of Paracoccidioidomycosis, Med. Mycol., 2024, 63(1), myae125,  DOI:10.1093/mmy/myae125.
271. M. Hunt, A. E. Mather, L. Sánchez-Busó, A. J. Page, J. Parkhill, J. A. Keane and S. R. Harris, ARIBA: Rapid Antimicrobial Resistance Genotyping Directly from Sequencing Reads, Microb. Genom., 2017, 3(10) DOI:10.1099/mgen.0.000131.
272. R. Rose, D. J. Nolan, D. Ashcraft, A. K. Feehan, L. Velez-Climent, C. Huston, B. Lain, S. Rosenthal, L. Miele, G. B. Fogel, G. Pankey, J. Garcia-Diaz and S. L. Lamers, Comparing Antimicrobial Resistant Genes and Phenotypes across Multiple Sequencing Platforms and Assays for Enterobacterales Clinical Isolates, BMC Microbiol., 2023, 23(1), 225,  DOI:10.1186/s12866-023-02975-x.
273. K. Juraschek, M. Borowiak, S. H. Tausch, B. Malorny, A. Käsbohrer, S. Otani, S. Schwarz, D. Meemken, C. Deneke and J. A. Hammerl, Outcome of Different Sequencing and Assembly Approaches on the Detection of Plasmids and Localization of Antimicrobial Resistance Genes in Commensal Escherichia Coli, Microorganisms, 2021, 9(3), 598,  DOI:10.3390/microorganisms9030598.
274. T. Weinmaier, R. Conzemius, Y. Bergman, S. Lewis, E. B. Jacobs, P. D. Tamma, A. Materna, J. Weinberger, S. Beisken and P. J. Simner, Validation and Application of Long-Read Whole-Genome Sequencing for Antimicrobial Resistance Gene Detection and Antimicrobial Susceptibility Testing, Antimicrob. Agents Chemother., 2023, 67(1), e01072–22,  DOI:10.1128/aac.01072-22.
275. A. Farooq, J. Kim, S. Raza, J. Jang, D. Han, M. J. Sadowsky and T. Unno, A Hybrid DNA Sequencing Approach Is Needed to Properly Link Genotype to Phenotype in Multi-Drug Resistant Bacteria, Environ. Pollut., 2021, 289, 117856,  DOI:10.1016/j.envpol.2021.117856.
276. B. Berbers, A. Saltykova, C. Garcia-Graells, P. Philipp, F. Arella, K. Marchal, R. Winand, K. Vanneste, N. H. C. Roosens and S. C. J. De Keersmaecker, Combining Short and, Long Read Sequencing to Characterize Antimicrobial Resistance Genes on Plasmids Applied to an Unauthorized Genetically Modified Bacillus, Sci. Rep., 2020, 10(1), 4310,  DOI:10.1038/s41598-020-61158-0.
277. D. C. De Bastiani, C. V. Silva, A. P. Christoff, G. N. F. Cruz, L. D. Tavares, L. S. R. De Araújo, B. M. Tomazini, B. Arns, F. T. Piastrelli, A. B. Cavalcanti, L. F. V. De Oliveira and A. J. Pereira, 16S rRNA Amplicon Sequencing and Antimicrobial Resistance Profile of Intensive Care Units Environment in 41 Brazilian Hospitals, Front. Public Health, 2024, 12, 1378413,  DOI:10.3389/fpubh.2024.1378413.
278. S. Algarni, D. D. Gudeta, D. Sopovski, J. Han, K. M. Feye and S. L. Foley, Transcriptomic Analyses of a Salmonella Enterica-Escherichia Coli Pair Following Exposure to Tetracycline during in Vitro Conjugation Experiments, Microbiol. Resour. Announce., 2024, 13(9), e00289–24,  DOI:10.1128/mra.00289-24.
279. K. K. Amoako, M. C. Thomas, F. Kong, T. W. Janzen, K. R. Hahn, M. J. Shields and N. Goji, Rapid Detection and Antimicrobial Resistance Gene Profiling of Yersinia Pestis Using Pyrosequencing Technology, J. Microbiol. Methods, 2012, 90(3), 228–234,  DOI:10.1016/j.mimet.2012.05.012.
280. K. Ajbani, S.-Y. G. Lin, C. Rodrigues, D. Nguyen, F. Arroyo, J. Kaping, L. Jackson, R. S. Garfein, D. Catanzaro, K. Eisenach, T. C. Victor, V. Crudu, M. T. Gler, N. Ismail, E. Desmond, A. Catanzaro and T. C. Rodwell, Evaluation of Pyrosequencing for Detecting Extensively Drug-Resistant Mycobacterium Tuberculosis among Clinical Isolates from Four High-Burden Countries, Antimicrob. Agents Chemother., 2015, 59(1), 414–420,  DOI:10.1128/AAC.03614-14.
281. D. S. Gao, X. Zhu and B. Lu, Development and Application of Sensitive, Specific, and Rapid CRISPR–Cas13−based Diagnosis, J. Med. Virol., 2021, 93(7), 4198–4204,  DOI:10.1002/jmv.26889.
282. K. Huang, H. Yu, Z. Chen, G. Lin, Z. Zhang, X. Zhang, Y. Dong, H. Chen, Z. Zhang, W. Ma, Y. Wu and T. Liu, CRISPR-Cas13a-Based Diagnostic Method for Chlamydia Trachomatis from Nongonococcal Urethritis, Bioanalysis, 2021, 13(11), 901–912,  DOI:10.4155/bio-2021-0022.
283. X. Bai, P. Gao, K. Qian, J. Yang, H. Deng, T. Fu, Y. Hu, M. Han, H. Zheng, X. Cao, Y. Liu, Y. Lu, A. Huang and Q. Long, A Highly Sensitive and Specific Detection Method for Mycobacterium Tuberculosis Fluoroquinolone Resistance Mutations Utilizing the CRISPR-Cas13a System, Front. Microbiol., 2022, 13, 847373,  DOI:10.3389/fmicb.2022.847373.
284. Y. Yang, K. E. Niehaus, T. M. Walker, Z. Iqbal, A. S. Walker, D. J. Wilson, T. E. A. Peto, D. W. Crook, E. G. Smith, T. Zhu and D. A. Clifton, Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data, Bioinformatics, 2018, 34(10), 1666–1671,  DOI:10.1093/bioinformatics/btx801.
285. Y. Ren, T. Chakraborty, S. Doijad, L. Falgenhauer, J. Falgenhauer, A. Goesmann, A.-C. Hauschild, O. Schwengers and D. Heider, Prediction of Antimicrobial Resistance Based on Whole-Genome Sequencing and Machine Learning, Bioinformatics, 2022, 38(2), 325–334,  DOI:10.1093/bioinformatics/btab681.
286. C. Wan, A. Qu, M. Li, R. Tang, L. Fu, X. Liu, P. Wang and C. Wu, Electrochemical Sensor for Directional Recognition and Measurement of Antibiotic Resistance Genes in Water, Anal. Chem., 2022, 94(2), 732–739,  DOI:10.1021/acs.analchem.1c03100.
287. N. Nordin, N. A. Yusof, J. Abdullah, S. Radu and R. Hushiarian, A Simple, Portable, Electrochemical Biosensor to Screen Shellfish for Vibrio Parahaemolyticus, AMB Express, 2017, 7(1), 41,  DOI:10.1186/s13568-017-0339-8.
288. H. M. Man, C. Omar, M. Freisa, D. Bouville, T. Baptiste, A.-M. Haghiri-Gosnet, H. Jacquier, I. Le Potier and J. Gamby, Microfluidics-Based Electrochemical Detection of Antimicrobial-Resistant DNA Sequence in Lysed Escherichia Coli Medium, ACS Electrochem., 2025, 1(6), 886–896,  DOI:10.1021/acselectrochem.4c00203.
289. S. Balasubramanian, I. B. Sorokulova, V. J. Vodyanoy and A. L. Simonian, Lytic Phage as a Specific and Selective Probe for Detection of Staphylococcus Aureus—A Surface Plasmon Resonance Spectroscopic Study, Biosens. Bioelectron., 2007, 22(6), 948–955,  DOI:10.1016/j.bios.2006.04.003.
290. J. Guo, J. Hou, Y. Wan, Z. Yang, Y. Li, Y. Zhu, K. Wang and W. Ding, Integrating Thermal Vibration and Local Surface Plasmon Resonance Effect Boosted “Symbiotic Co-Evolution” for Efficient Solar Evaporation, Antimicrobial and Antibiotic Resistance Genes Removal, Water Res., 2025, 284, 123997,  DOI:10.1016/j.watres.2025.123997.
291. R. K. Mayaka and E. C. Alocilja, Genomic Nano-Biosensor for Rapid Detection of the Carbapenem-Resistant Gene BlaNDM-1 in Carbapenemase-Producing Bacteria, Nanoscale Adv., 2025, 7(9), 2518–2527,  10.1039/D4NA00798K.
292. F. Gütgemann, A. Heuvelink, A. Müller, Y. Churin, R. Buter, A. Jung, A. Feberwee, J. Wiegel, F. Kumm, A. S. Braun, M. Yue, E. Soriano-Vargas, S. Swanepoel, N. Botteldoorn, M. Kirchner and C. Kehrenberg, Recommendation of a Standardized Broth Microdilution Method for Antimicrobial Susceptibility Testing of Avibacterium Paragallinarum and Resistance Monitoring, J. Clin. Microbiol., 2024, 62(3), e01011–23,  DOI:10.1128/jcm.01011-23.
293. N. Kadeřábková, A. J. S. Mahmood and D. A. I. Mavridou, Antibiotic Susceptibility Testing Using Minimum Inhibitory Concentration (MIC) Assays, npj Antimicrob. Resist., 2024, 2(1), 37,  DOI:10.1038/s44259-024-00051-6.
294. C. Lange, S. Schubert, J. Jung, M. Kostrzewa and K. Sparbier, Quantitative Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Rapid Resistance Detection, J. Clin. Microbiol., 2014, 52(12), 4155–4162,  DOI:10.1128/JCM.01872-14.
295. S.-Y. Hsieh, C.-L. Tseng, Y.-S. Lee, A.-J. Kuo, C.-F. Sun, Y.-H. Lin and J.-K. Chen, Highly Efficient Classification and Identification of Human Pathogenic Bacteria by MALDI-TOF MS, Mol. Cell. Proteomics, 2008, 7(2), 448–456,  DOI:10.1074/mcp.M700339-MCP200.
296. M. Vatanshenassan, T. Boekhout, C. Lass-Flörl, M. Lackner, S. Schubert, M. Kostrzewa and K. Sparbier, Proof of Concept for MBT ASTRA, a Rapid Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry (MALDI-TOF MS)-Based Method To Detect Caspofungin Resistance in Candida Albicans and Candida Glabrata, J. Clin. Microbiol., 2018, 56(9), e00420-18,  DOI:10.1128/JCM.00420-18.
297. H. A. M. Hendawy, R. M. Youssif, N. N. Salama, A. S. Fayed and M. Y. Salem, Challenge Approach of an Inexpensive Electrochemical Sensor for Rapid Selective Determination of Two Non–classical β–Lactams in Presence of Different Degradants and Interference Substances, Electroanalysis, 2017, 29(12), 2708–2718,  DOI:10.1002/elan.201700431.
298. J. D. Besant, E. H. Sargent and S. O. Kelley, Rapid Electrochemical Phenotypic Profiling of Antibiotic-Resistant Bacteria, Lab Chip, 2015, 15(13), 2799–2807,  10.1039/C5LC00375J.
299. C. R. Nemr, S. J. Smith, W. Liu, A. H. Mepham, R. M. Mohamadi, M. Labib and S. O. Kelley, Nanoparticle-Mediated Capture and Electrochemical Detection of Methicillin-Resistant Staphylococcus Aureus, Anal. Chem., 2019, 91(4), 2847–2853,  DOI:10.1021/acs.analchem.8b04792.
300. M. Rochelet, S. Solanas, L. Betelli, C. Neuwirth, F. Vienney and A. Hartmann, Amperometric Detection of Extended-Spectrum β-Lactamase Activity: Application to the Characterization of Resistant E. Coli Strains, Analyst, 2015, 140(10), 3551–3556,  10.1039/C4AN01786B.
301. A. J. Bramburger, R. S. Brown, J. Haley and J. J. A. Ridal, New, Automated Rapid Fluorometric Method for the Detection of Escherichia Coli in Recreational Waters, J. Gt. Lakes Res., 2015, 41(1), 298–302,  DOI:10.1016/j.jglr.2014.12.008.
302. J. Chen, M. Tomasek, A. Cruz, M. L. Faron, D. Liu, W. H. Rodgers and V. Gau, Feasibility and Potential Significance of Rapid in Vitro Qualitative Phenotypic Antimicrobial Susceptibility Testing of Gram-Negative Bacilli with the ProMax System, PLoS One, 2021, 16(3), e0249203,  DOI:10.1371/journal.pone.0249203.
303. L. A. Neely, M. Audeh, N. A. Phung, M. Min, A. Suchocki, D. Plourde, M. Blanco, V. Demas, L. R. Skewis, T. Anagnostou, J. J. Coleman, P. Wellman, E. Mylonakis and T. J. Lowery, T2 Magnetic Resonance Enables Nanoparticle-Mediated Rapid Detection of Candidemia in Whole Blood, Sci. Transl. Med., 2013, 5(182), 182ra54,  DOI:10.1126/scitranslmed.3005377.
304. N. A. Masdor, Z. Altintas and I. E. Tothill, Sensitive Detection of Campylobacter Jejuni Using Nanoparticles Enhanced QCM Sensor, Biosens. Bioelectron., 2016, 78, 328–336,  DOI:10.1016/j.bios.2015.11.033.
305. B. Zhou, Y. Hao, S. Chen and P. Yang, A Quartz Crystal Microbalance Modified with Antibody-Coated Silver Nanoparticles Acting as Mass Signal Amplifiers for Real-Time Monitoring of Three Latent Tuberculosis Infection Biomarkers, Microchim. Acta, 2019, 186(4), 212,  DOI:10.1007/s00604-019-3319-7.
306. A. Chokshi, Z. Sifri, D. Cennimo and H. Horng, Global Contributors to Antibiotic Resistance, J. Global Infect. Dis., 2019, 11(1), 36–42,  DOI:10.4103/jgid.jgid_110_18.
307. N. D. Friedman, E. Temkin and Y. Carmeli, The Negative Impact of Antibiotic Resistance, Clin. Microbiol. Infect., 2016, 22(5), 416–422,  DOI:10.1016/j.cmi.2015.12.002.
308. G. Birgand, P. Dhar and A. Holmes, The Threat of Antimicrobial Resistance in Surgical Care: The Surgeon's Role and Ownership of Antimicrobial Stewardship, Br. J. Surg., 2023, 110(12), 1567–1569,  DOI:10.1093/bjs/znad302.
309. F. Bert, B. Larroque, C. Paugam-Burtz, F. Dondero, F. Durand, E. Marcon, J. Belghiti, R. Moreau and M.-H. Nicolas-Chanoine, Pretransplant Fecal Carriage of Extended-Spectrum β-Lactamase–Producing Enterobacteriaceae and Infection after Liver Transplant, France, Emerging Infect. Dis., 2012, 18(6), 908–916,  DOI:10.3201/eid1806.110139.
310. M. So and L. Walti, Challenges of Antimicrobial Resistance and Stewardship in Solid Organ Transplant Patients, Curr. Infect. Dis. Rep., 2022, 24(5), 63–75,  DOI:10.1007/s11908-022-00778-1.
311. E. Korzeniewska and M. Harnisz, Sources, Occurrence, and Environmental Risk Assessment of Antibiotics and Antimicrobial-Resistant Bacteria in Aquatic Environments of Poland, in Polish River Basins and Lakes – Part II, ed. E., Korzeniewska and M. Harnisz, The Handbook of Environmental Chemistry, Springer International Publishing, Cham, 2020, Vol. 87, pp. 179–193.  DOI:10.1007/978-3-030-12139-6_9.
312. A. Carbonne, I. Arnaud, S. Maugat, N. Marty, C. Dumartin, X. Bertrand, O. Bajolet, A. Savey, T. Fosse, M. Eveillard, H. Senechal, B. Coignard, P. Astagneau and V. Jarlier, on behalf of the MDRB Surveillance National Steering Group (BMR-Raisin). National Multidrug-Resistant Bacteria (MDRB) Surveillance in France through the RAISIN Network: A 9 Year Experience, J. Antimicrob. Chemother., 2013, 68(4), 954–959,  DOI:10.1093/jac/dks464.
313. M.-H. Nicolas-Chanoine, C. Gruson, S. Bialek-Davenet, X. Bertrand, F. Thomas-Jean, F. Bert, M. Moyat, E. Meiller, E. Marcon, N. Danchin, L. Noussair, R. Moreau and V. Leflon-Guibout, 10-Fold Increase (2006–11) in the Rate of Healthy Subjects with Extended-Spectrum -Lactamase-Producing Escherichia Coli Faecal Carriage in a Parisian Check-up Centre, J. Antimicrob. Chemother., 2013, 68(3), 562–568,  DOI:10.1093/jac/dks429.
314. A. Birgy, R. Cohen, C. Levy, P. Bidet, C. Courroux, M. Benani, F. Thollot and E. Bingen, Community Faecal Carriage of Extended-Spectrum Beta-Lactamase-Producing Enterobacteriaceaein French Children, BMC Infect. Dis., 2012, 12(1), 315,  DOI:10.1186/1471-2334-12-315.
315. P. Mullany, Functional Metagenomics for the Investigation of Antibiotic Resistance, Virulence, 2014, 5(3), 443–447,  DOI:10.4161/viru.28196.
316. M. Andreescu, Molecular Insights Into the Role of Gut Microbiota in Antibiotic Therapy Selection and Resistance Mitigation, Cureus, 2023, 15(12), e50318,  DOI:10.7759/cureus.50318.
317. G. K. Paulus, L. M. Hornstra, N. Alygizakis, J. Slobodnik, N. Thomaidis and G. Medema, The Impact of On-Site Hospital Wastewater Treatment on the Downstream Communal Wastewater System in Terms of Antibiotics and Antibiotic Resistance Genes, Int. J. Hyg. Environ. Health, 2019, 222(4), 635–644,  DOI:10.1016/j.ijheh.2019.01.004.
318. N. Li, G.-P. Sheng, Y.-Z. Lu, R. J. Zeng and H.-Q. Yu, Removal of Antibiotic Resistance Genes from Wastewater Treatment Plant Effluent by Coagulation, Water Res., 2017, 111, 204–212,  DOI:10.1016/j.watres.2017.01.010.
319. M. M. McConnell, L. T. Hansen, R. C. Jamieson, K. D. Neudorf, C. K. Yost and A. Tong, Removal of Antibiotic Resistance Genes in Two Tertiary Level Municipal Wastewater Treatment Plants, Sci. Total Environ., 2018, 643, 292–300,  DOI:10.1016/j.scitotenv.2018.06.212.
320. Z. Wang, L. Fu, J.-D. Gu, S. Deng, C. Huang and L. Luo, The Factors Controlling Antibiotic Resistance Genes in Different Treatment Processes of Mainstream Full-Scale Wastewater Treatment Plants, Sci. Total Environ., 2023, 900, 165815,  DOI:10.1016/j.scitotenv.2023.165815.
321. S. Richards, E. Paterson, P. J. A. Withers and M. Stutter, Septic Tank Discharges as Multi-Pollutant Hotspots in Catchments, Sci. Total Environ., 2016, 542, 854–863,  DOI:10.1016/j.scitotenv.2015.10.160.
322. J. W. A. Charrois, Private Drinking Water Supplies: Challenges for Public Health, Can. Med. Assoc. J., 2010, 182(10), 1061–1064,  DOI:10.1503/cmaj.090956.
323. S. Baer, W. Robertson, J. Spoelstra and S. Schiff, Phosphorus and Nitrogen Loading to Lake Huron from Septic Systems at Grand Bend, ON, J. Gt. Lakes Res., 2019, 45(3), 642–650,  DOI:10.1016/j.jglr.2019.03.003.
324. D. N. D. Samaraweera, X. Liu, G. Zhong, T. Priyadarshana, R. N. Malik, G. Zhang, M. S. Khorram, Z. Zhu and X. Peng, Antibiotics in Two Municipal Sewage Treatment Plants in Sri Lanka: Occurrence, Consumption and Removal Efficiency, Emerg. Contam., 2019, 5, 272–278,  DOI:10.1016/j.emcon.2019.08.001.
325. S. Galvin, F. Boyle, P. Hickey, A. Vellinga, D. Morris and M. Cormican, Enumeration and Characterization of Antimicrobial-Resistant Escherichia Coli Bacteria in Effluent from Municipal, Hospital, and Secondary Treatment Facility Sources, Appl. Environ. Microbiol., 2010, 76(14), 4772–4779,  DOI:10.1128/AEM.02898-09.
326. Wastewater Systems Effluent Regulations, https://laws-lois.justice.gc.ca/eng/regulations/sor-2012-139/fulltext.html (accessed 2025-04-02).
327. K. E. Raven, C. Ludden, T. Gouliouris, B. Blane, P. Naydenova, N. M. Brown, J. Parkhill and S. J. Peacock, Genomic Surveillance of Escherichia Coli in Municipal Wastewater Treatment Plants as an Indicator of Clinically Relevant Pathogens and Their Resistance Genes, Microb. Genomics, 2019, 5(5), e000267,  DOI:10.1099/mgen.0.000267.
328. O. Muter, L. Dubova, O. Kassien, J. Cakane and I. Alsina, Application of the Sewage Sludge in Agriculture: Soil Fertility, Technoeconomic, and Life-Cycle Assessment, in Hazardous Waste Management, ed. R. Banu Jeyakumar, K. Sankarapandian and Y. Kannah Ravi, IntechOpen, 2022.  DOI:10.5772/intechopen.104264.
329. M. Patra, B. Pandey and S. K. Dubey, Prevalence of Diverse Antimicrobial Resistance Genes and Bacteria in Sewage Treatment Plant-Derived Sludge Environment, FEMS Microbes, 2024, 5, xtae004,  DOI:10.1093/femsmc/xtae004.
330. Water/Septic Issues…in Rural/ Micro-Communities, 2018, https://www.waynecarson.ca/issues/rural-micro-communities-waterseptic-issue/ (accessed 2025-04-04).
331. L. Tan, C. Zhang, F. Liu, P. Chen, X. Wei, H. Li, G. Yi, Y. Xu and X. Zheng, Three-Compartment Septic Tanks as Sustainable on-Site Treatment Facilities? Watch out for the Potential Dissemination of Human-Associated Pathogens and Antibiotic Resistance, J. Environ. Manage., 2021, 300, 113709,  DOI:10.1016/j.jenvman.2021.113709.
332. Y. Lan, W. Tang, S. Dye and E. Delmelle, A Web-Based Spatial Decision Support System for Monitoring the Risk of Water Contamination in Private Wells, Ann. GIS, 2020, 26(3), 293–309,  DOI:10.1080/19475683.2020.1798508.
333. F. P. Brennan, V. O'Flaherty, G. Kramers, J. Grant and K. G. Richards, Long-Term Persistence and Leaching of Escherichia Coli in Temperate Maritime Soils, Appl. Environ. Microbiol., 2010, 76(5), 1449–1455,  DOI:10.1128/AEM.02335-09.
334. R. Sender, S. Fuchs and R. Milo, Revised Estimates for the Number of Human and Bacteria Cells in the Body, PLoS Biol., 2016, 14(8), e1002533,  DOI:10.1371/journal.pbio.1002533.
335. T. Aw, Environmental Aspects and Features of Critical Pathogen Groups, in Water and Sanitation for the 21st Century: Health and Microbiological Aspects of Excreta and Wastewater Management (Global Water Pathogen Project), ed. Michigan State University, J. B. Rose, B. Jiménez Cisneros and UNESCO - International Hydrological Programme, Michigan State University, 2019.  DOI:10.14321/waterpathogens.2.
336. I. Kempf, E. Jouy and C. Chauvin, Colistin Use and Colistin Resistance in Bacteria from Animals, Int. J. Antimicrob. Agents, 2016, 48(6), 598–606,  DOI:10.1016/j.ijantimicag.2016.09.016.
337. Food and Agriculture Organization of the United Nations, The State of World Fisheries and Aquaculture 2024 - Blue Transformation in action, in Food and Agriculture Organization of the United Nations, Rome, 2024,  DOI:10.4060/cd0683en.
338. N. B. Jadeja and A. Worrich, From Gut to Mud: Dissemination of Antimicrobial Resistance between Animal and Agricultural Niches, Environ. Microbiol., 2022, 24(8), 3290–3306,  DOI:10.1111/1462-2920.15927.
339. F. S. Kibenge, Emerging Viruses in Aquaculture, Curr. Opin. Virol., 2019, 34, 97–103,  DOI:10.1016/j.coviro.2018.12.008.
340. A. Kläui, U. Bütikofer, J. Naskova, E. Wagner and E. Marti, Fresh Produce as a Reservoir of Antimicrobial Resistance Genes: A Case Study of Switzerland, Sci. Total Environ., 2024, 907, 167671,  DOI:10.1016/j.scitotenv.2023.167671.
341. M. Fox, R. Christley, C. Lupo, H. Moore, M. Service and K. Campbell, Preventing and Mitigating Farmed Bivalve Disease: A Northern Ireland Case Study, Aquacult. Int., 2020, 28(6), 2397–2417,  DOI:10.1007/s10499-020-00597-y.
342. D. M. Prendergast, Á O'Doherty, C. M. Burgess, N. Howe, F. McMahon, D. Murphy, F. Leonard, D. Morris, C. Harrington, A. Carty, J. Moriarty and M. Gutierrez, Critically Important Antimicrobial Resistant Enterobacteriaceae in Irish Farm Effluent and Their Removal in Integrated Constructed Wetlands, Sci. Total Environ., 2022, 806, 151269,  DOI:10.1016/j.scitotenv.2021.151269.
343. O. O. Alegbeleye and A. S. Sant'Ana, Manure-Borne Pathogens as an Important Source of Water Contamination: An Update on the Dynamics of Pathogen Survival/Transport as Well as Practical Risk Mitigation Strategies, Int. J. Hyg. Environ. Health, 2020, 227, 113524,  DOI:10.1016/j.ijheh.2020.113524.
344. Y. Zhang, Y. Zhang, J. Xie, C. Yuan, D. Zhu and X. Shi, Vertical Migration and Leaching Behavior of Antibiotic Resistance Genes in Soil during Rainfall: Impact by Long-Term Fertilization, Water Res., 2024, 267, 122508,  DOI:10.1016/j.watres.2024.122508.
345. WOAH, Eighth Annual Report on Antimicrobial Agents Intended for Use in Animals, WOAH (World Organisation for Animal Health), 2024.  DOI:10.20506/amu.3474.
346. Z. Ardakani, M. Aragrande and M. Canali, Global Antimicrobial Use in Livestock Farming: An Estimate for Cattle, Chickens, and Pigs, Animal, 2024, 18(2), 101060,  DOI:10.1016/j.animal.2023.101060.
347. N. Udikovic-Kolic, F. Wichmann, N. A. Broderick and J. Handelsman, Bloom of Resident Antibiotic-Resistant Bacteria in Soil Following Manure Fertilization, Proc. Natl. Acad. Sci. U. S. A., 2014, 111(42), 15202–15207,  DOI:10.1073/pnas.1409836111.
348. C. Tyrrell, T. T. Do, R. J. Leigh, C. M. Burgess, F. P. Brennan and F. Walsh, Differential Impact of Swine, Bovine and Poultry Manure on the Microbiome and Resistome of Agricultural Grassland, Sci. Total Environ., 2023, 886, 163926,  DOI:10.1016/j.scitotenv.2023.163926.
349. European Food Safety Authority (EFSA), M. Aerts, A. Battisti, R. Hendriksen, I. Kempf, C. Teale, B. Tenhagen, K. Veldman, D. Wasyl, B. Guerra, E. Liébana, D. Thomas-López and P. Belœil, Technical Specifications on Harmonised Monitoring of Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Food–producing Animals and Food, EFSA J., 2019, 17(6), e05709,  DOI:10.2903/j.efsa.2019.5709.
350. M. Pan and L. M. Chu, Leaching Behavior of Veterinary Antibiotics in Animal Manure-Applied Soils, Sci. Total Environ., 2017, 579, 466–473,  DOI:10.1016/j.scitotenv.2016.11.072.
351. F. Wang, W. Han, S. Chen, W. Dong, M. Qiao, C. Hu and B. Liu, Fifteen-Year Application of Manure and Chemical Fertilizers Differently Impacts Soil ARGs and Microbial Community Structure, Front. Microbiol., 2020, 11, 62,  DOI:10.3389/fmicb.2020.00062.
352. Q. Chen, X. An, H. Li, J. Su, Y. Ma and Y.-G. Zhu, Long-Term Field Application of Sewage Sludge Increases the Abundance of Antibiotic Resistance Genes in Soil, Environ. Int., 2016, 92–93, 1–10,  DOI:10.1016/j.envint.2016.03.026.
353. X.-R. Huang, R. Neilson, L.-Y. Yang, S.-Y.-D. Zhou, H. Li, Y.-G. Zhu and X.-R. Yang, Urban Greenspace Types Influence the Microbial Community Assembly and Antibiotic Resistome More in the Phyllosphere than in the Soil, Chemosphere, 2023, 338, 139533,  DOI:10.1016/j.chemosphere.2023.139533.
354. M. M. Escursell, A. Roschi, T. H. M. Smits and F. Rezzonico, Characterization and Direct Molecular Discrimination of rpsL Mutations Leading to High Streptomycin Resistance in Erwinia Amylovora, J. Plant Pathol., 2021, 103(S1), 99–108,  DOI:10.1007/s42161-020-00600-8.
355. P. L. Sholberg, K. E. Bedford, P. Haag and P. Randall, Survey of Erwinia Amylovora Isolates from British Columbia for Resistance to Bactericides and Virulence on Apple, Can. J. Plant Pathol., 2001, 23(1), 60–67,  DOI:10.1080/07060660109506910.
356. J. Schroeder, L. Kammann, M. Helfrich, C. C. Tebbe and C. Poeplau, Impact of Common Sample Pre-Treatments on Key Soil Microbial Properties, Soil Biol. Biochem., 2021, 160, 108321,  DOI:10.1016/j.soilbio.2021.108321.
357. J.-Q. Su, B. Wei, W.-Y. Ou-Yang, F.-Y. Huang, Y. Zhao, H.-J. Xu and Y.-G. Zhu, Antibiotic Resistome and Its Association with Bacterial Communities during Sewage Sludge Composting, Environ. Sci. Technol., 2015, 49(12), 7356–7363,  DOI:10.1021/acs.est.5b01012.
358. S. Kaza, L. C. Yao, P. Bhada-Tata and F. Van Woerden, What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, World Bank, Washington, DC, 2018.  DOI:10.1596/978-1-4648-1329-0.
359. L. M. Chu, K. C. Cheung and M. H. Wong, Variations in the Chemical Properties of Landfill Leachate, Environ. Manage., 1994, 18(1), 105–117,  DOI:10.1007/BF02393753.
360. L. Song, L. Li, S. Yang, J. Lan, H. He, S. P. McElmurry and Y. Zhao, Sulfamethoxazole, Tetracycline and Oxytetracycline and Related Antibiotic Resistance Genes in a Large-Scale Landfill, China, Sci. Total Environ., 2016, 551–552, 9–15,  DOI:10.1016/j.scitotenv.2016.02.007.
361. Z. Chen, L. Yao, F. Sun, Y. Zhu, N. Li, D. Shen and M. Wang, Antibiotic Resistance Genes Are Enriched with Prolonged Age of Refuse in Small and Medium-Sized Landfill Systems, Environ. Res., 2021, 197, 111194,  DOI:10.1016/j.envres.2021.111194.
362. X. Yang, C. Jia, Y. Yao, T. Yang and S. Shao, Precise Management and Control around the Landfill Integrating Artificial Intelligence and Groundwater Pollution Risks, Chemosphere, 2024, 364, 143185,  DOI:10.1016/j.chemosphere.2024.143185.
363. K. Peer, B. Hubbard, M. Monti, P. Vander Kelen and A. K. Werner, The Private Well Water Climate Impact Index: Characterization of Community-Level Climate-Related Hazards and Vulnerability in the Continental United States, Sci. Total Environ., 2024, 957, 177409,  DOI:10.1016/j.scitotenv.2024.177409.
364. M. C. Mattioli, K. M. Benedict, J. Murphy, A. Kahler, K. E. Kline, A. Longenberger, P. K. Mitchell, S. Watkins, P. Berger, O. C. Shanks, C. E. Barrett, L. Barclay, A. J. Hall, V. Hill and A. Weltman, Identifying Septic Pollution Exposure Routes during a Waterborne Norovirus Outbreak - A New Application for Human-Associated Microbial Source Tracking qPCR, J. Microbiol. Methods, 2021, 180, 106091,  DOI:10.1016/j.mimet.2020.106091.
365. J. O'Dwyer, P. Hynds, M. Pot, C. C. Adley and M. P. Ryan, Evaluation of Levels of Antibiotic Resistance in Groundwater-Derived E. Coli Isolates in the Midwest of Ireland and Elucidation of Potential Predictors of Resistance, Hydrogeol. J., 2017, 25(4), 939–951,  DOI:10.1007/s10040-017-1546-8.
366. A. Jurado, A. Margareto, E. Pujades, E. Vázquez-Suñé and M. S. Diaz-Cruz, Fate and Risk Assessment of Sulfonamides and Metabolites in Urban Groundwater, Environ. Pollut., 2020, 267, 115480,  DOI:10.1016/j.envpol.2020.115480.
367. G. Ding, G. Chen, Y. Liu, M. Li and X. Liu, Occurrence and Risk Assessment of Fluoroquinolone Antibiotics in Reclaimed Water and Receiving Groundwater with Different Replenishment Pathways, Sci. Total Environ., 2020, 738, 139802,  DOI:10.1016/j.scitotenv.2020.139802.
368. World Health Organization (WHO), Global Antibiotic Resistance Surveillance Report 2025: WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS), World Health Organization (WHO), Geneva, 1st edn, 2025.
369. Polltical Declaration of the High-Level Meeting on the Antimicrobial Resistance; Resolution A/79/L.5; UN General Assembly, 2024.
370. Universal Declaration of Human Rights; 217 A (III); UN General Assembly, 1948, https://www.un.org/en/about-us/universal-declaration-of-human-rights.

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