A mini-review of anti-hepatitis B virus activity of medicinal plants
Bibliographic record
Abstract
Medicinal plants are of undoubted value, as they have been used for centuries to treat various diseases and health disorders in almost every part of the world. In several studies, the use of medicinal plants was found effective in treatment of infectious and non-infectious diseases. The World Health Organization has been working for many years to identify all surviving medicinal plants on the earth. An important step has also been taken by the Natural Health Product Regulation of Canada for promotion and usages of natural products. At present, the rapidly growing population of the world is facing many challenges from various infectious diseases that are associated with hepatitis A, B and C virus, human immunodeficiency virus, influenza virus, dengue virus and new emerging viruses. Hepatitis B virus causes a severe and frequently transmittable disease of the liver. Millions of people worldwide suffer from hepatitis B virus (HBV) infection. The drugs available on the market for the treatment of hepatitis B are not sufficient and also cause side effects in patients suffering from HBV infection. The pharmaceutical companies are searching for suitable alternative and natural inhibitors of HBV. Therefore, it is important to explore and use plants as a source of new medicines to treat this infectious disease, because single plants contain a priceless pool of active ingredients which could help in the production of pharmaceutical-grade peptides or proteins. However, the knowledge of the antiviral activity of medicinal plants is still limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".