Smartphone–based enzymatic biosensor for oral fluid L-lactate detection in one minute using confined multilayer paper reflectometry

Add time:09/10/2019    Source:sciencedirect.com

The development of smartphone–based biosensors for point-of-care testing (POCT) applications allows realizing “all in one” instruments, with large potential distribution among the general population. With this respect, paper color-based detection performed by reflectance measurement is the most popular, simple, inexpensive and straightforward method. Despite the large number of scientific publications related to these biosensors, they still suffer from a poor detectability and reproducibility related to inhomogeneity of color development, which leads to low assay reproducibility. To overcome these problems, we propose a smartphone paper-based biosensor, in which all the reagents necessary to complete the analysis are co-entrapped on paper in a “wafer”-like bilayer film of polyelectrolytes (Poly (allyl amine hydrochloride/poly(sodium 4-styrene sulfonate)). Using a 3D printing low-cost technology we fabricated the smartphone-based device that consists in a cover accessory attached to the smartphone and incorporating a light diffuser over the flash to improve the image quality, a mini dark box and a disposable analytical cartridge containing all the reagents necessary for the complete analysis. The biosensor was developed exploiting coupled enzyme reactions for quantifying L-lactate in oral fluid, which is considered a biomarker of poor tissue perfusion, a key element in the management of severe sepsis, septic shock and in sports performance evaluation. The developed method is sensitive, rapid, and it allows detecting L-lactate in oral fluid in the relevant physiological range, with a limit of detection of 0.1 mmol L−1. The extreme simplicity of assay execution (no reagents need to be added) and flexibility of fabrication of the device, together with the high assay versatility (any oxidase can be coupled with HRP-based color change reaction) make our approach suitable for the realization of smartphone-based biosensors able to non-invasively detect a large variety of analytes of clinical interest.

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