EFFECTS OF TRAPIDIL ON THE HEALING OF COLONIC ANASTOMOSES IN AN EXPERIMENTAL RAT MODEL
Bibliographic record
Abstract
BACKGROUND: Trapidil has various properties including vasodilatation, inhibition of lipid peroxidation and platelet aggregation as well as, and reduction of, the inflammatory response to injury. The aim of the present study was to investigate the effects of trapidil on dexamethasone-impaired colonic anastomotic healing in an experimental rat model. METHODS: Twenty-four Wistar rats underwent colonic transsection and primary anastomosis. Rats were divided into four groups of six: group 1 (G1), control; group 2 (G2) trapidil, 8 mg/kg per day intravenously; group 3 (G3) dexamethasone, 0.1 mg/kg per day intramuscularly; and group 4 (G4) dexamethasone 0.1 mg/kg intramuscularly and trapidil 8 mg/kg intravenously per day, for 1 week. Anastomotic bursting pressure, hydroxyproline level, histopathological grading, malondialdehyde and nitrite/nitrate levels were determined. RESULTS: Dexamethasone-impaired anastomotic healing was found to be improved by trapidil administration in terms of anastomotic bursting pressure and hydroxyproline content (P = 0.026, and P = 0.017). In addition, histopathological examination revealed an increase in fibroblast proliferation and collagen deposition (P = 0.004, and P = 0.015) and a decrease in leucocyte infiltration (P = 0.004). Moreover, serum nitrite/nitrate and malondialdehyde levels decreased when G3 was compared to G4 (P < 0.001, P = 0.38). CONCLUSIONS: Trapidil may improve the dexamethasone-impaired anastomotic healing due to its preventive effects on inflammatory response and lipid peroxidation in rats.
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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 | 0.001 | 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) | 0.002 | 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".