COVID-19 rapid diagnostic test for instrumentation-free virus detection in saliva
Bibliographic record
Abstract
Widespread home point of care (POC) testing for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is pivotal to control the coronavirus disease 2019 (COVID-19). The reverse transcription polymerase chain reaction, as the current gold-standard tool for diagnosis of COVID-19, even with excellent sensitivity and specificity is not well-suited for home POC diagnostics as it is expensive, hard to administer and limited to a peripheral instrument. Here, we developed a fully-autonomous capillary microfluidic chip, called domino capillaric circuits (DCC), to perform on-chip enzyme-linked immunosorbent assay. The DCC enables fluidic operations such as sample metering, aliquoting, reagent incubation and washing. We also developed a cell phone readout platform to analyze the time-insensitive colorimetric signal and used commercially-available antibodies and materials to detect SARS-CoV-2 nucleocapsid protein in saliva sample with the limit of detection of 0.28 ng/mL. The DCC is fully-automated, user-friendly, enables rapid and quantitative detection of SARS-CoV-2 in saliva and has the potential to be employed for home POC diagnostics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".