Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (Sars-Cov-2) variants in a perpetual state of evolution are persistently challenging the development of medical therapeutics. Continuing beyond the mutation escape of variants requires a specific, stable, point-of-care, modifiable, and low-cost therapeutic reagent for both prophylactic treatment and clinical treatment. The nucleic acid-based approach, aptamer, has become one of the most competitive candidates for this highdemand anti-covid treatment. As current substantial research has consolidated its optimistic biosensor role in the field of detection and diagnostics for Sars-Cov-2, it is undoubtedly worth exploring aptamers as neutralizing agents. The applicability of aptamers with refined advantages should not only allow more possibilities in screening and diagnosis but also confer promising capabilities in neutralization, chimeric therapy, delivery, and vaccines for COVID-19. Therefore, the paper, through the method of literature review, reveals the current state of coronavirus and aptamer, summarizes the recent developments in theranostic aptamers, anti-Sars-Cov-2 neutralizing aptamers, and combined aptamers, and the prospect of aptamer research, including its challenges and focus. The paper concludes that aptamer-based biosensors, rapid antigen tests, and treatments are promising priorities against COVID-19 as diagnostic-aimed and neutralizing-aimed aptamers have been developed during the past two years. Although RBD-targeted and multivalent aptamers partly dampen the burden of nonspecificity and low effectivity, pushing into the “in vivo” testing stage and tackling frequent mutation escape should be the future research focus.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".