Hepatoprotective effect of linagliptin against liver fibrosis induced by carbon tetrachloride in mice
Bibliographic record
Abstract
The current study aimed to investigate linagliptin for its potential role in the prevention of liver fibrosis progression. Balb-C mice were randomly allocated into five groups (10 each): (i) control; (ii) mice were injected intraperitoneally with 50 μL carbon tetrachloride (CCl4) in corn oil in a dose of 0.6 μL/g three times per week for four weeks; (iii) linagliptin was administered orally in a daily dose of 10 mg/kg simultaneously with CCl4; (iv) silymarin was administered orally in a daily dose of 200 mg/kg concomitantly with CCl4; and (v) only linagliptin was administered. Hepatic injury was manifested in the CCl4 group by elevation of biochemical parameters (alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP)), and hepatic fibrosis was evident histopathologically by increased METAVIR score and immunostaining expression of alpha-smooth muscle actin (α-SMA), as well as increased liver tissue oxidative stress parameters, transforming growth factor-β1 (TGF-β1), and mammalian target of rapamycin (mTOR). Linagliptin was able to stop the progression of liver fibrosis, evident histopathologically with reduced METAVIR score and α-SMA expression. The possible mechanism may be via suppression of oxidative stress, TGF-β1, and mTOR, which was associated with improvement of serum biochemical parameters ALT and AST. In conclusion, linagliptin might help to protect the liver against persistent injury-related consequences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".