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Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

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Abstract

This work describes a fully-integrated portable microfluidic analysis system for real-time monitoring of dynamic changes in glucose and lactate occurring in the brain as a result of cardiac arrest and resuscitation. Brain metabolites are sampled using FDA-approved microdialysis probes and coupled to a high-temporal resolution 3D printed microfluidic chip housing glucose and lactate biosensors. The microfluidic biosensors are integrated with a wireless 2-channel potentiostat forming a compact analysis system that is ideal for use in a crowded operating theatre. Data are transmitted to a custom-written app running on a tablet for real-time visualisation of metabolic trends. In a proof-of-concept porcine model of cardiac arrest, the integrated analysis system proved reliable in a challenging environment resembling a clinical setting; noise levels were found to be comparable with those seen in the lab and were not affected by major clinical interventions such as defibrillation of the heart. Using this system, we were able, for the first time, to measure changes in brain glucose and lactate levels caused by cardiac arrest and resuscitation; the system was sensitive to clinical interventions such as infusion of adrenaline. Trends suggest that cardiopulmonary resuscitation alone does not meet the high energy demands of the brain as metabolite levels only return to their values preceding cardiac arrest upon return of spontaneous circulation.

Graphical abstract: Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

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Article information


Submitted
30 Sep 2019
Accepted
28 Nov 2019
First published
24 Jan 2020

Analyst, 2020, Advance Article
Article type
Paper

Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

S. A. N. Gowers, I. C. Samper, D. R. K. Murray, G. K. Smith, S. Jeyaprakash, M. L. Rogers, M. Karlsson, M. H. Olsen, K. Møller and M. G. Boutelle, Analyst, 2020, Advance Article , DOI: 10.1039/C9AN01950B

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