Updates on Traditional Medicinal Plants for Hepatocellular Carcinoma
Bibliographic record
Abstract
Aim: Hepatocellular carcinoma (HCC) is a major worldwide problem primarily caused by hepa titis B and C virus infection. End stage liver cancer treatment options are limited thus requiring expensive liver transplantation which is not available in many countries. Methods: Several herbal compounds and herbal composite formulas have been studied through in-vitro and in vivo as an anti-HCC agent, enhancing our knowledge about their biological functions and targets. In this article, arecent update on the herbal medicine has been provided with reference to liver cancer. Results: For the sake of clarity, the effective herbal compounds, clinical studies of herbal composite formula, cell culture, and animal model studies safety are discussed. The effects of many herbal active compounds of Annona atemoya, Andrographis paniculata, Boerha viadiffusa, Piper longum, Podophyllum hexandrum, Phyllanthus amarus, and Terminalia chebula, and herbal composite formula on autophagy, apoptosis, antioxidant, and inflammation characteristicshave been provided. Conclusion: This will enhance our un-derstanding on the prevention and treatment of HCC by herbal active compounds and herbal composite formulas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".