Preclinical Models for Investigation of Herbal Medicines in Liver Diseases: Update and Perspective
Bibliographic record
Abstract
Liver disease results from a dynamic pathological process associated with cellular and genetic alterations, which may progress stepwise to liver dysfunction. Commonly, liver disease begins with hepatocyte injury, followed by persistent episodes of cellular regeneration, inflammation, and hepatocyte death that may ultimately lead to nonreversible liver failure. For centuries, herbal remedies have been used for a variety of liver diseases and recent studies have identified the active compounds that may interact with liver disease-associated targets. Further study on the herbal remedies may lead to the formulation of next generation medicines with hepatoprotective, antifibrotic, and anticancer properties. Still, the pharmacological actions of vast majority of herbal remedies remain unknown; thus, extensive preclinical studies are important. In this review, we summarize progress made over the last five years of the most commonly used preclinical models of liver diseases that are used to screen for curative herbal medicines for nonalcoholic fatty liver disease, liver fibrosis/cirrhosis, and liver. We also summarize the proposed mechanisms associated with the observed liver-protective, antifibrotic, and anticancer actions of several promising herbal medicines and discuss the challenges faced in this research field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".