Efficacy of Alanyl-Glutamine in Preventing Mesh-Induced Adhesions: A Rat Model Study
Bibliographic record
Abstract
Study objectives: This study aimed to evaluate the effectiveness of alanyl-glutamine in preventing intraperitoneal adhesions in the presence of Marlex and Prolene meshes in a rat model. Design: This experimental study utilized a randomized controlled trial design. Setting: The study was conducted at University of Saskatchewan, involving a laboratory animal facility. Interventions: Twenty-four Wistar rats weighing over 300g were randomly assigned to three groups: Group 1 underwent laparotomy with cecal ligation and puncture and received Marlex or Prolene mesh, Group 2 underwent the same procedure with mesh plus alanyl-glutamine treatment, and Group 3 served as controls with laparotomy only. Adhesion formation was evaluated using histological staining techniques. Results: Rats treated with alanyl-glutamine showed significantly fewer adhesions compared to those without treatment, as evidenced by lower Zuhlke adhesion scores and histological analysis. Adhesions were absent at the six-week follow-up in the treatment group. Conclusion: Alanyl-glutamine effectively reduced adhesion formation in the presence of Marlex and Prolene meshes in this experimental rat model. These findings suggest a potential clinical application of alanyl-glutamine in enhancing surgical outcomes by mitigating postoperative adhesions associated with mesh placement.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".