An Evaluation of the Potential of Heparin to Inhibit the Viral Entry of SARS-CoV-2
Bibliographic record
Abstract
Heparin is an anticoagulant medicine that prevents the formation of harmful blood clots in the vessels. Following the outbreak of the novel coronavirus disease 2019 (COVID-19), heparin has helped to improve the health of affected patients beyond its anticoagulant effects. The potential antiviral activity of heparin has attracted speculation due to its highly sulfated profile, which allows it to have a high binding affinity to a wide range of viral components. Heparin’s successful binding to the ZIKA virus, human immunodeficiency virus, as well as the SARS CoV and MERS CoV spike proteins have demonstrated its potential to inhibit the entry of SARS-CoV-2 into the body. A high degree of sequence homology also enables heparin to have inhibitory binding potential on viral components. The SARS-CoV-2 virus exhibits significant differences in its spike glycoprotein (SGP) sequence compared to other coronaviruses. The SGP sequence in SARS-CoV-2 contains additional potential glycosaminoglycan (GAG) binding domains that may drive differences in the attachment and entry process of the virus. Findings from unbiased computational ligand docking simulations, pseudotyped spike protein experiments, and cell to cell fusion assays have also opened possibilities to investigate the antiviral properties of heparin in clinical trials
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".