DataVXSmallPaper
journal article·2023-12Open access

Deep Learning‐Enabled Multiplexed Point‐of‐Care Sensor using a Paper‐Based Fluorescence Vertical Flow Assay (Small 51/2023)

Artem Goncharov, Hyou‐Arm Joung, Rajesh Ghosh, Gyeo‐Re Han, Zachary S. Ballard, Quinn Maloney, Alexandra Bell, Chew Tin Zar Aung, Omai B. Garner, Dino Di Carlo, Aydogan Ozcan

Small· Volume 19· Issue 51
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Source abstract

Abstract

Cardiac Biomarkers In article number 2300617, Aydogan Ozcan and co-workers demonstrate a deep learning-enabled multiplexed point-of-care sensor using a paper-based fluorescence vertical flow assay to simultaneously quantify three cardiac biomarkers in serum. Using a low-cost mobile reader, this multiplexed sensor quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 μL of serum sample per patient.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Biosensors and Analytical Detection · SARS-CoV-2 detection and testing · Advanced biosensing and bioanalysis techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Electrical & Computer Engineering Department University of California Los Angeles CA 90095 USA

Bioengineering Department University of California Los Angeles CA 90095 USA

Chemistry & Biochemistry Department University of California Los Angeles CA 90095 USA

Microbiology, Immunology & Molecular Genetics University of California Los Angeles CA 90095 USA

Department of Pathology and Laboratory Medicine University of California Los Angeles CA 90095 USA

California NanoSystems Institute (CNSI) University of California Los Angeles CA 90095 USA

References

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