The green tea catechin EGCG provides proof-of-concept for a pan-coronavirus entry inhibitor
Bibliographic record
Abstract
Abstract The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has emphasized the serious threat to human health posed by emerging coronaviruses. Effective antiviral countermeasures are urgently needed as vaccine development and deployment against an emerging coronavirus takes time, even in the best-case scenario. The green tea catechin, epigallocatechin gallate (EGCG), has broad spectrum antiviral activity. We demonstrate here that EGCG prevents murine and human coronavirus infection and blocks the entry of lentiviral particles pseudotyped with spike proteins from highly pathogenic coronaviruses, as well as a bat coronavirus poised to emerge into humans. We show that EGCG treatment reduces coronavirus attachment to target cell surfaces. Our results demonstrate the potential for the development of pan-coronavirus attachment inhibitors to protect against current and future emerging coronaviruses.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".