Inhibition of SARS-CoV-2 Infection in Human Airway Epithelium with a Xeno-Nucleic Acid Aptamer
Bibliographic record
Abstract
Background: SARS-CoV-2, the agent responsible for the COVID-19 pandemic, enters cells through viral spike glycoprotein binding to the cellular receptor, angiotensin-converting enzyme 2 (ACE2). Given the lack of effective antivirals targeting SARS-CoV-2, we previously utilized systematic evolution of ligands by exponential enrichment (SELEX) and selected fluoro-arabino nucleic acid (FANA) aptamer R8-9 that was able to block the interaction between the viral receptor-binding domain and ACE2. Methods: . Results: model of human airway epithelium (HAE) and SARS-CoV-2 virus, we show that FANA-R8-9 significantly reduces viral infection when added either at the time of inoculation, or several hours later. These results were specific to the R8-9 sequence, not the xeno-nucleic acid utilized to make the aptamer. Importantly, we also show that FANA-R8-9 is stable in HAE culture secretions and has no overt cytotoxic effects. Conclusions: Together, these results suggest that FANA-R8-9 effectively prevents infection by specific SARS-CoV-2 variants and indicate that aptamer technology could be utilized to target other clinically-relevant viruses in the respiratory mucosa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".