Pre-Clinical Evaluation of the Antiviral Activity of Epigalocatechin-3-Gallate, a Component of Green Tea, against Influenza A(H1N1)pdm Viruses
Bibliographic record
Abstract
Influenza antiviral drugs are important tools in our fight against both annual influenza epidemics and pandemics. Polyphenols are a group of compounds found in plants, some of which have demonstrated promising antiviral activity. Previous in vitro and mouse studies have outlined the anti-influenza virus effectiveness of the polyphenol epigallocatechin-3-gallate (EGCG); however, no study has utilised the ferret model, which is considered the gold-standard for influenza antiviral studies. This study aimed to explore the antiviral efficacy of EGCG in vitro and in ferrets. We first performed studies in Madin-Darby Canine Kidney (MDCK) and human lung carcinoma (Calu-3) cells, which demonstrated antiviral activity. In MDCK cells, we observed a selective index (SI, CC50/IC50) of 77 (290 µM/3.8 µM) and 96 (290 µM/3.0 µM) against A/California/07/2009 and A/Victoria/2570/2019 (H1N1)pdm09 influenza virus, respectively. Calu-3 cells demonstrated a SI of 16 (420 µM/26 µM) and 18 (420 µM/24 µM). Ferrets infected with A/California/07/2009 influenza virus and treated with EGCG (500 mg/kg/day for 4 days) had no change in respiratory tissue viral titres, in contrast to oseltamivir treatment, which significantly reduced viral load in the lungs of treated animals. Therefore, we demonstrated that although EGCG showed antiviral activity in vitro against influenza viruses, the drug failed to impair viral replication in the respiratory tract of ferrets.
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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) | 0.001 | 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 | 0.001 |
| 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".