Diosmin attenuates radiation-induced hepatic fibrosis by boosting PPAR-γ expression and hampering miR-17-5p-activated canonical Wnt–β-catenin signaling
Bibliographic record
Abstract
BACKGROUND: Liver fibrosis is one of the major complications from upper right quadrant radiotherapy. MicroRNA-17-5p (miR-17-5p) is hypothesized to act as a regulator of hepatic stellate cell (HSCs) activation by activation of the canonical Wnt-β-catenin pathway. Diosmin (Dios), a citrus bioflavonoid, is known to possess potent antioxidant, anti-inflammatory, and anti-apoptotic properties. PURPOSE: To explore the molecular mechanisms that underlie radiation-induced liver fibrosis, and to evaluate the possible influence of Dios on the miR-17-5p-Wnt-β-catenin signaling axis during fibrogenesis provoked by irradiation (IRR) in rats. Also, the effect of Dios on hepatic peroxisome proliferator activated receptor-γ (PPAR-γ) expression as a regulator for HSC activation was considered. METHODS: (per oral) of Dios were administered to IRR-exposed rats (overall dose of 12 Gy on 6 fractions of 2 Gy each) for 6 successive weeks. RESULTS: Data analysis revealed that Dios treatment mitigated oxidative stress, enhanced antioxidant defenses, alleviated hepatic inflammatory responses, abrogated pro-fibrogenic cytokines, and stimulated PPAR-γ expression. Dios treatment repressed the miR-17-5p activated Wnt-β-catenin signaling induced by IRR. Moreover, Dios treatment restored the normal hepatic architecture and reversed pathological alterations induced by IRR. CONCLUSION: We hypothesize that the stimulation of PPAR-γ expression and interference with miR-17-5p activated Wnt-β-catenin signaling mediates the antifibrotic properties of Dios.
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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) | 0.000 | 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".