Aptamers for SARS‐CoV‐2: Isolation, Characterization, and Diagnostic and Therapeutic Developments
Bibliographic record
Abstract
The SARS-CoV-2 virus and COVID-19 pandemic continue to demand effective diagnostic and therapeutic solutions. Finding these solutions requires highly functional molecular recognition elements. Nucleic acid aptamers represent a possible solution. Characterized by their high affinity and specificity, aptamers can be rapidly identified from random-sequence nucleic acid libraries. Over the past two years, many labs around the world have rushed to create diverse aptamers that target two important structural proteins of SARS-CoV-2: the spike (S) protein and nucleocapsid (N) protein. These have led to the identification of many aptamers that show real promise for the development of diagnostic tests and therapeutic agents for SARS-CoV-2. Herein we review all these developments, with a special focus on the development of diverse aptasensors for detecting SARS-CoV-2. These include electrochemical and optical sensors, lateral flow devices, and aptamer-linked immobilized sorbent assays.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".