Issue 42, 2022

Degradation of hydrogel beads for the detection of serum bicarbonate levels for the diagnosis of metabolic alkalosis at the point of care

Abstract

In this work, we present a novel point-of-care hydrogel-based diagnostic device for the rapid detection of elevated bicarbonate levels in serum for the diagnosis of mild to severe cases of metabolic alkalosis. Our system consists of hydrogel beads composed of calcium alginate and the nonionic polymer dextran. This assay utilizes the reaction of sodium bicarbonate and citric acid to produce citrate, a metal chelator capable of competitively binding to calcium cations in the gel matrix to trigger hydrogel degradation. This results in successful detection of elevated bicarbonate concentrations in less than one hour. Specifically, critically high bicarbonate concentrations of 50, 45, and 40 mmol L−1 in human serum were detected in as little as 10, 15, and 20 min, respectively. To demonstrate the assay's feasibility for use in resource-limited settings, we developed a simple electronic device that achieved similar results and could be used by untrained individuals with no lab equipment and minimal power. To our knowledge, this is the first demonstration of the use of nonionic polymers to synthesize and improve the morphology of calcium alginate hydrogel beads using a simple processing method that involves minimal labor and equipment. The simplified bead synthesis protocol combined with the user-friendly device allows for the rapid detection of metabolic alkalosis at the point of care.

Graphical abstract: Degradation of hydrogel beads for the detection of serum bicarbonate levels for the diagnosis of metabolic alkalosis at the point of care

Supplementary files

Article information

Article type
Paper
Submitted
28 Jul 2022
Accepted
10 Oct 2022
First published
11 Oct 2022

Soft Matter, 2022,18, 8147-8156

Degradation of hydrogel beads for the detection of serum bicarbonate levels for the diagnosis of metabolic alkalosis at the point of care

P. Pandolfi, H. Zhang, Y. E. Nadalin, M. E. Prasetyo, A. A. Toubian, B. M. Wu and D. T. Kamei, Soft Matter, 2022, 18, 8147 DOI: 10.1039/D2SM01014C

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