Inhibition of influenza A virus infection by ginsenosides
Bibliographic record
Abstract
Influenza viruses cause mild to severe respiratory infections in humans. Due to efficient means of transmission, the viruses infect human population on a large scale. Apart from vaccines, antiviral drugs are used to control infection; neuraminidase inhibitors are thought to be the first choice of treatment, particularly for severe cases. Rapidly evolving and emerging influenza viruses with increased frequency of viral resistance to these drugs stress the need to explore novel antiviral compounds. In this study, we investigated antiviral activity of ginseng extract and ginsenosides, the ginseng-derived triterpene and saponin compounds, against 2009 pandemic H1N1 virus in vitro and in vivo. Our data showed that treatment of mice with ginsenosides protected the animals from lethal 2009 pandemic H1N1 infection and lowered viral titers in animal lungs. Mechanistic studies revealed that ginsenosides interact with viral hemagglutinin protein and prevent the attachment of virus with α 2-3' sialic acid receptors present on host cell surfaces. The interference in the viral attachment process subsequently minimizes viral entry into the cells and decreases the severity of the viral infection. We also describe that sugar moieties present in ginsenosides are indispensible for their attachment with viral HA protein. On the basis of our observations, we can say that ginsenosides are promising candidates for the development of antiviral drugs for influenza viruses.
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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".