<i>N</i>-acetylcysteine a possible protector against indomethacin-induced peptic ulcer: crosstalk between antioxidant, anti-inflammatory, and antiapoptotic mechanisms
Bibliographic record
Abstract
This study investigated the gastroprotective effects of N-acetylcysteine (NAC) against indomethacin-induced gastric ulcer in rats. Ulceration was induced by a single oral administration of indomethacin (30 mg/kg). 50 male albino rats were allocated into 5 equal groups: control group received normal saline orally, indomethacin group rats received normal saline orally for 5 days and indomethacin (50 mg/kg) on the last day, ranitidine group received ranitidine (reference drug) orally for 5 days (50 mg/kg) before receiving indomethacin (50 mg/kg) on the last day, and NAC groups received NAC orally at 300 and 500 mg/kg, respectively, for 5 days before receiving indomethacin (50 mg/kg) on the last day. Gastric tissue interleukin-1β (IL-1β), interferon-γ (IFN-γ), and caspase-3 levels were immunoassayed. Total thiol (T-SH), myeloperoxidase (MPO), and glucose-6-phosphate dehydrogenase (G6PD) were determined by spectrophotometry. Cytokine-induced neutrophil chemoattractant 2α (CINC-2α) gene expression was evaluated in addition to Bcl-2 immunohistochemistry. Pretreatment with NAC improved the inflammatory, apoptotic, and redox status in a dose-dependent manner particularly in NAC 500 mg/kg pretreated group. These results show a role for NAC in improving indomethacin-induced gastric ulceration via antioxidative, antiapoptotic, and anti-inflammatory interactive mechanisms.
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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 | 0.000 |
| 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".