Tetramethylpyrazine ameliorates hepatic fibrosis through autophagy-mediated inflammation
Bibliographic record
Abstract
BACKGROUND: Imbalanced immune response and hepatic fibrosis are key factors related to the progression of chronic liver diseases. Tetramethylpyrazine (TMP), a natural alkaloid, has been widely used for treating liver injury. In this study, we explored the effect of TMP on hepatic fibrosis and the related mechanisms regulating autophagy. METHODS: and platelet-derived growth factor (PDGF). Staining with haematoxylin and eosin (HE), Masson's stain, and TUNEL were performed for pathological diagnosis. ELISA, Western blotting, and immunofluorescence analyses were conducted to determine the expression levels of the specific markers for fibrosis, autophagy, inflammation, and signalling pathways. RESULTS: TMP treatment significantly rescued pathological injury and hepatic fibrosis. It also alleviated imbalances in the immune system, accumulation of extracellular matrix, and autophagy signals in hepatic fibrosis. At the same time, we found that application of the autophagy inducer rapamycin enhanced the therapeutic effect of TMP, whereas the autophagy inhibitor 3-methyladenine, PI3K pathway inhibitor LY294002, and AKT pathway agonist SC79 did the opposite. CONCLUSIONS: TMP exerts therapeutic effects in hepatic fibrosis mainly through promoting autophagy to ameliorate inflammation by inhibiting the AKT-mTOR signalling pathway, providing a new perspective for the treatment of chronic liver diseases.
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 | 0.001 |
| 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".