Reduction of asymmetric dimethylarginine in the protective effects of rutaecarpine on gastric mucosal injury
Bibliographic record
Abstract
Our recent study has shown that asymmetric dimethylarginine (ADMA) plays an important role in facilitating gastric mucosal injury by multiple factors. To explore whether the protection of rutaecarpine against gastric mucosal injury is related to reduction of ADMA content, a model of ethanol-induced gastric mucosal injury in rats was selected for this study. The ulcer index, the content of ADMA and NO, and the activity of dimethylarginine dimethylaminohydrolase (DDAH) in gastric tissues were measured in vivo after pretreatment with rutaecarpine. The in vitro effect of rutaecarpine on the release of calcitonin gene-related peptide (CGRP) and NO from isolated gastric tissues was also determined. The results showed that ethanol significantly increased the ulcer index, decreased the DDAH activity and the NO level, and elevated the ADMA level, which was attenuated by pretreatment with rutaecarpine (0.6 mg/kg or 1.2 mg/kg). In the isolated gastric tissues, rutaecarpine significantly increased the release of both CGRP and NO; the release of NO, but not CGRP, was abolished in the presence of l-NAME (10(-4) mol/L). The present results suggest that rutaecarpine protects the gastric mucosa against injury induced by ethanol and that the gastroprotection of rutaecarpine is related to reduction of ADMA levels through stimulating the release of CGRP.
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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) | 0.001 | 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) | 0.002 | 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".