Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) outbreak has become a global pandemic. The deleterious effects of coronavirus have prompted the development of diagnostic tools to manage the spread of disease. While conventional technologies such as quantitative real time polymerase chain reaction (qRT-PCR) have been broadly used to detect COVID-19, they are time-consuming, labor-intensive and are unavailable in remote settings. Point-of-care (POC) biosensors, including chip-based and paper-based biosensors are typically low-cost and user-friendly, which offer tremendous potential for rapid medical diagnosis. This mini review article discusses the recent advances in POC biosensors for COVID-19. First, the development of POC biosensors which are made of polydimethylsiloxane (PDMS), papers, and other flexible materials such as textile, film, and carbon nanosheets are reviewed. The advantages of each biosensors along with the commercially available COVID-19 biosensors are highlighted. Lastly, the existing challenges and future perspectives of developing robust POC biosensors to rapidly identify and manage the spread of COVID-19 are briefly discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".