Mercaptoethylguanidine Attenuates Caustic Esophageal Injury in Rats; A Role for Scavenging of Peroxynitrite
Bibliographic record
Abstract
Introduction After ingestion of caustic material, tissue damage is caused by reactive oxygen species and reactive nitrogen species. Mercaptoethylguanidine (MEG) is a scavenger of peroxynitrite. Thus, this study was designed whether MEG has a beneficial effect on caustic esophageal injury. Methods 45 rats were allocated into 3 groups; sham‐operated, untreatment and treatment groups. Caustic esophageal burn was created by instilling 15% NaOH in the distal esophagus. The treatment group treated with 10 mg/kg/day MEG i.p. for 5 days. All rats were killed at 28 days. Efficacy of the treatment was assessed by histopathologically and biochemically. Results The stenosis index and the histopathologic damage score were significantly lower in the MEG‐treatment group which showed a correlation with tissue hydroxyproline level. In the untreatment group, tissue oxidative stres parameters (malondialdehyde and protein carbonyl content) were significantly higher, antioxidant enzymes activities (SOD and GSH‐Px) were significantly lower than trreatment group. Urinary Nitrate and Nitrite (NOx) levels increased in the treatment and un‐treatment group at the first three days. Conclusion Peroxynitrites play an important role in the healing process of caustic esophagitis. MEG might be used a potential adjuvant agent in treatment of esophageal caustic burn by modulating antioxidant defense mechanism.
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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".