Therapeutic efficacy of <i>Hypericum perforatum</i> L. extract for mice infected with an influenza A virus
Bibliographic record
Abstract
Hypericum perforatum L., a plant used in Chinese herbal medicine, has been proven effective against many viral diseases. In the present study, the therapeutic efficacy of an extract of H. perforatum (HPE) against influenza A virus (IAV) was investigated in mice. Whether HPE would be a promising agent for influenza treatment was evaluated by measuring the protection rate, mean survival days, lung index, and viral titer, as well as the secretion of IL-6, interleukin-10 (IL-10), tumour necrosis factor-α (TNF-α), and interferon-gamma (IFN-γ) in lung tissue and serum on days 3 and 5 post-infection. The results showed that HPE could reduce the lung index and viral titer of mice infected with IAV, decrease mortality, and prolong the mean survival time. HPE decreased the concentration of IL-6 and TNF-α in lung tissue and serum on day 5 post-infection. In contrast, HPE enhanced the lung and serum levels of IL-10 and IFN-γ on the days 3 and 5 post-infection. Our study indicates that HPE has significant therapeutic efficacy for mice infected with IAV. The possible reasons for these results were concluded to be pertaining to up-regulating the expression of IL-10 and IFN-γ, and down-regulating the secretion of IL-6 and TNF-α in lung and serum.
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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 | 0.001 |
| 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".