Hydrolysable tannins of tropical almond show antifibrotic effects in TGF‐β1‐induced hepatic stellate cells
Bibliographic record
Abstract
BACKGROUND: Persistent activation of hepatic stellate cells (HSC-T6) has been known to cause liver fibrosis. In this study, our objective was to investigate the effects of chebulagic acid and chebulinic acid, two hydrolysable tannins of tropical almond (Terminalia chebula) fruits, on collagen synthesis and signal transduction in transforming growth factor-β1-stimulated HSC-T6 cells. The expression of Smad2, Smad3, Smad4, collagen I(α1)/III, and plasminogen activator inhibitor 1 (PAI-1) mRNAs was determined by reverse-transcription polymerase chain reaction and their protein levels were assessed by western blotting. RESULTS: Results showed that chebulagic acid and chebulinic acid at 20 µmol L(-1) exhibited cytotoxic and anti-proliferative effects on HSC-T6 cells. They also significantly decreased the expression of Smd2, Smad3 and Smad4, and the synthesis of collagen, procollagen I (α1) and III, as well as suppressing the activation of PAI-1; these events consequently facilitated the resolution of fibrosis. CONCLUSION: These results indicate that both chebulagic acid and chebulinic acid possess antifibrotic activity, and their mechanism of action could be through the inhibition of the Smad pathway.
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 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".