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Point-of-care biomarker quantification enabled by sample-specific calibration
Author(s) -
Monica P. McNerney,
Yan Zhang,
Paige Steppe,
Adam D. Silverman,
Michael C. Jewett,
Mark P. Styczynski
Publication year - 2019
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.aax4473
Subject(s) - calibration , sample (material) , biomarker , computer science , point of care , computational biology , medicine , statistics , chromatography , biology , chemistry , mathematics , pathology , biochemistry
Easy-to-perform, relatively inexpensive blood diagnostics have transformed at-home healthcare for some patients, but they require analytical equipment and are not easily adapted to measuring other biomarkers. The requirement for reliable quantification in complex sample types (such as blood) has been a critical roadblock in developing and deploying inexpensive, minimal-equipment diagnostics. Here, we developed a platform for inexpensive, easy-to-use diagnostics that uses cell-free expression to generate colored readouts that are visible to the naked eye, yet quantitative and robust to the interference effects seen in complex samples. We achieved this via a parallelized calibration scheme that uses the patient sample to generate custom reference curves. We used this approach to quantify a clinically relevant micronutrient and to quantify nucleic acids, demonstrating a generalizable platform for low-cost quantitative diagnostics.

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