Instrument-free single-step direct estimation of the plasma glucose level from one drop of blood using smartphone-interfaced analytics on a paper strip. Issue 23 (8th November 2022)

• Record Type: Journal Article
• Authors: Biswas, Sujay K.; Chatterjee, Subhamoy; Laha, Sampad; Pakira, Victor; Som, Nirmal K.; Saha, Satadal; Chakraborty, Suman
• Is Part Of: Lab on a chip. Volume 22:Issue 23(2022)
• Page Start: 4666
• Page End: 4679
• Publication Date: 2022-11-08
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d2lc00824f
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : We demonstrated an instrument-free miniaturized adaptation of the laboratory gold standard methodology for direct estimation of plasma glucose from a drop of blood using a low-cost single-user-step paper-strip sensor interfaced with a smartphone. Abstract : We demonstrated an instrument-free miniaturized adaptation of the laboratory gold standard methodology for the direct estimation of plasma glucose from a drop of whole blood using a low-cost single-user-step paper-strip sensor interfaced with a smartphone. Unlike a majority of the existing glucose meters that use whole blood-based indirect sensing technologies, our direct adaptation of the gold-standard laboratory benchmark could eliminate the possibilities of cross interference with other analytes present in the whole blood by facilitating an in situ plasma separation, capillary flow and colorimetric reaction occurring concomitantly, without incurring additional device complexity or embodiment. The test reagents were dispensed in lyophilized form, and the resulting paper strips were found to be stable over three months stored in a normal freezer, rendering easy adaptability commensurate with the constrained supply chains in extreme resource-poor settings. Quantitative results could be arrived at via a completely-automated mobile-app-based image analytics interface developed using dynamic machine learning, obviating manual interpretation. The tests were demonstrated to be of high efficacy, even when executed by minimally trained frontline personnel having no special skill of drawing precise volume of blood, on deployment at under-resourced community centres having no in-built or accessible healthcare infrastructure. Clinical validation using 220 numbers of human blood samples in a double-blinded manner evidenced sensitivity and specificity of 98.11% and 96.7%, respectively, as compared to the results obtained from a laboratory-benchmarked biochemistry analyser, establishing its efficacy for public health and community disease management in resource-limited settings without any quality compromise of the test outcome.