Issue 18, 2022

CandyCollect: at-home saliva sampling for capture of respiratory pathogens

Abstract

Streptococcus pyogenes is a major human-specific bacterial pathogen and a common cause of a wide range of symptoms from mild infection such as pharyngitis (commonly called strep throat) to life-threatening invasive infection and post-infectious sequelae. Traditional methods for diagnosis include collecting a sample using a pharyngeal swab, which can cause discomfort and even discourage adults and children from seeking proper testing and treatment in the clinic. Saliva samples are an alternative to pharyngeal swabs. To improve the testing experience for strep throat, we developed a novel lollipop-inspired sampling platform (called CandyCollect) to capture bacteria in saliva. The device can be used in clinics or in the home and shipped back to a lab for analysis, integrating with telemedicine. CandyCollect is designed to capture bacteria on an oxygen plasma treated polystyrene surface embedded with flavoring substances to enhance the experience for children and inform the required time to complete the sampling process. In addition, the open channel structure prevents the tongue from scraping and removing the captured bacteria. The flavoring substances did not affect bacterial capture and the device has a shelf life of at least 2 months (with experiments ongoing to extend the shelf life). We performed a usability study with 17 participants who provided feedback on the device design and the dissolving time of the candy. This technology and advanced processing techniques, including polymerase chain reaction (PCR), will enable user-friendly and effective diagnosis of streptococcal pharyngitis.

Graphical abstract: CandyCollect: at-home saliva sampling for capture of respiratory pathogens

Supplementary files

Article information

Article type
Paper
Submitted
15 dec 2021
Accepted
13 jún 2022
First published
19 aug 2022

Lab Chip, 2022,22, 3555-3564

CandyCollect: at-home saliva sampling for capture of respiratory pathogens

U. N. Lee, X. Su, D. L. Hieber, W. Tu, A. M. McManamen, M. G. Takezawa, G. W. Hassan, T. C. Chan, K. N. Adams, E. R. Wald, G. P. DeMuri, E. Berthier, A. B. Theberge and S. Thongpang, Lab Chip, 2022, 22, 3555 DOI: 10.1039/D1LC01132D

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