The intervention of <i>tert</i>-butylhydroquinone protects ethanol-induced gastric ulcer in type II diabetic rats: the role of Nrf2 pathway
Bibliographic record
Abstract
Ethanol consumption increases the prevalence of gastric ulcer (GU) in rats with type II diabetes (T2D). Induction of GU by absolute ethanol (5 mL/kg or 3.94 g/kg) in the animal model resembles human ulcer characteristics. The aim was to investigate the role of the nuclear factor erythroid 2–related factor 2 (Nrf2) pathway in the treatment of GU in diabetic condition. The rats were exposed to absolute ethanol 1 h before sacrifice and T2D was induced by combined exposure of high-fat diet and low dose streptozotocin. Pretreatment of tert-butylhydroquinone (tBHQ) (25 and 50 mg/kg), metformin (500 mg/kg), and omeprazole (20 mg/kg) were given once daily for last three consecutive weeks. In ethanol-exposed diabetic rats, pretreatment with tBHQ, omeprazole, and metformin reduced gastric mucosal lesion, ulcer index, histological alterations, malondialdehyde level, and apoptosis. Furthermore, the intervention of tBHQ, omeprazole, and metformin improved the integrity of the stomach mucosa, glutathione, gastric pH, collagen, and goblet cells. tBHQ treatment improved ethanol-induced alterations of Nrf2, catalase, heat shock protein 70 (HSP70), NF-κB, and endothelin-1 expressions in diabetic rats. In diabetic conditions, the incidence of GU is increased due to elevated levels of reactive oxygen species, inflammatory mediators, depleted levels of cellular antioxidants, and altered gastric parameters. The tBHQ intervention could be a rational strategy to protect these changes.
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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".