COVID-19 Diagnostic Strategies Part II: Protein-Based Technologies
Bibliographic record
Abstract
After the initiation of the current outbreak, humans' lives have been profoundly impacted by COVID-19. During the first months, no rapid and reliable detecting tool was readily available to sufficiently respond to the requirement of massive testing. In this situation, when the development of an effective vaccine requires at least a few months, it is crucial to be prepared by developing and commercializing affordable, accurate, rapid and adaptable biosensors not only to fight Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) but also to be armed to avoid the pandemic in the earliest stages in the future. The COVID-19 diagnostic tools are categorized into two main groups of Nucleic Acid (NA)-based and protein-based tests. To date, nucleic acid-based detection has been announced as the gold-standard strategy for coronavirus detection; however, protein-based tests are promising alternatives for rapid and large-scale screening of susceptible groups. In this review, we discuss the current protein-based biosensing tools, the research advances and the potential protein-detecting strategies for COVID-19 detection. This narrative review aims to highlight the importance of the diagnostic tests, encourage the academic research groups and the companies to eliminate the shortcomings of the current techniques and step forward to mass-producing reliable point-of-care (POC) and point-of-need (PON) adaptable diagnostic tools for large-scale screening in the future outbreaks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".