Handheld Microfluidic Filtration Platform Enables Rapid, Low‐Cost, and Robust Self‐Testing of SARS‐CoV‐2 Virus
Bibliographic record
Abstract
Abstract Here, a novel microfluidic test kit combining ultrahigh throughput hydrodynamic filtration and sandwich immunoassay is reported. Specifically, nano and microbeads coated with two different, noncompetitive antibodies, are used to capture the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) nucleocapsid (N) proteins simultaneously, forming larger complexes. Microfluidic filtration discards free nanobeads but retains antigen‐bridged complexes in the observation zone, where a display of red color indicates the presence of antigen in the sample. This testing platform exhibits high throughput separation (<30 s) and enrichment of antigen that exceeds the traditional lateral flow assays or microfluidic assays, with a low limit of detection (LoD) < 100 copies mL‐1. In two rounds of clinical trials conducted in December 2020 and August 2021, the assays demonstrate high sensitivities of 95.4% and 100%, respectively, which proves this microfluidic test kit is capable of detecting SARS‐CoV‐2 virus variants evolved over significant periods of time. Furthermore, the mass‐produced chip can be fabricated at a cost of $0.98/test and the robust design allows the chip to be reused for over 50 times. All of these features make the microfluidic test kit particularly suitable for areas with inadequate medical infrastructure and a shortage of laboratory resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".