Resveratrol and curcumin as protective agents in an experimental rat model of intestinal ischemia and reperfusion
Bibliographic record
Abstract
The aim of this study was to evaluate the protective effects of resveratrol and curcumin in an experimental rat model of intestinal ischemia-reperfusion (I/R). Forty-eight adult Wistar rats were used: 12 animals undergoing the sham surgery and 36 animals undergoing laparotomy, with 15 min of mesentric artery clamping. The animals from the latter group (n = 12) were pretreated, for 1 week, with vehicle (CTR), resveratrol (RES), and curcumin (CUR). After 1 h and 6 h of reperfusion, respectively, cyclooxigenase (COX)-2, mucin-1, E-cadherin, nuclear factor (NK)-κB expressions, and tumor necrosis factor related apoptosis-inducing ligand (TRAIL) were assessed in the small intestine. Oxidative stress markers were determined in tissue homogenate and serum, and histopathological analysis was performed. Pretreatment with RES decreased the expression of COX-2 and NF-κB at both intervals and increased E-cadherin (p < 0.05) and mucin-1 production after 1 h. CUR had a beneficial effect on COX-2, NF-κB, and E-cadherin expressions, both after 1 h and after 6 h (p < 0.0001). The two compounds increased TRAIL levels and had a protective effect on oxidative stress and histopathological lesions, both after 1 h and after 6 h. Our results suggested that RES and CUR had beneficial effects in intestinal I/R and may represent a promising option for complementary treatment of this pathological condition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".