The Effect of a Hydrogen Sulfide (H <sub>2</sub> S)‐Releasing Non‐ Steroidal Anti‐Inflammatory Drug (NSAID) on Gastric and Renal Damage in Streptozotocin (STZ)‐Induced Diabetic Rats
Bibliographic record
Abstract
Non‐steroidal anti‐inflammatory drugs (NSAIDs) induce gastrointestinal (GI) and renal damage, which is exacerbated in diabetes. Hydrogen sulfide (H 2 S) counteracts adverse GI effects, so we investigated the GI and renal effects of naproxen and an H 2 S‐releasing NSAID, ATB‐346, in a model of diabetes. Diabetes was induced in male Wistar rats (n = 4–5) by streptozotocin (STZ) treatment. After two weeks of hyperglycemia, rats received vehicle, naproxen sodium (10 mg/kg) or ATB‐346 (14.5 mg/kg) for 9 doses over 4.5 days. Stomach and small intestine (SI) were removed to score hemorrhagic damage. Blood urea nitrogen (BUN) and creatinine analysis were performed in serum and urine, respectively. mRNA levels in renal tissue were assessed by quantitative RT‐PCR. No gastric damage was observed in any group, though extensive SI damage was observed in the naproxen‐treated group (p<0.05). No difference in markers of renal dysfunction after dosing with any drug. A 3‐ to 4‐fold increase in eNOS mRNA expression was observed in all groups compared to vehicle and we found a >;7‐fold increase in COX‐2 mRNA expression in kidneys of both naproxen and ATB‐346–treated rats. Together, these data indicate that ATB‐346 treatment confers a protective effect on the SI in STZ‐induced diabetic rats, whilst not inducing any more renal changes than naproxen. Supported by the Canadian Institutes of Health Research.
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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) | 0.000 | 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".