Effects of mTOR Inhibitors in Prevention of Abdominal Adhesions
Bibliographic record
Abstract
PURPOSE OF THE STUDY: Postsurgical adhesions can occur after laparotomy and can cause morbidity. Local delivery of sirolimus prevented adhesion formation in various experiments. We analyzed the impact of orally dosed mammalian target of rapamycin inhibitors on abdominal adhesion formation and wound tensile strength in an experimental model. MATERIALS AND METHODS: Wistar albino rats were divided into sirolimus, everolimus, and control groups (n = 6 per group). Experimental animals underwent midline laparotomy and adhesion induction procedure which included cecum abrasion and mesh implantation. Animals were administered oral sirolimus (4 mg/kg), everolimus (3 mg/kg), or placebo starting on postoperative day 1. Treatments were given until postoperative day 7. At postoperative day 21, adhesions were scored. Meshes were resected with the attached abdominal wall and cecal segment and stained with Sirius red for collagen density analysis. Midline scars were excised for tensile strength measurement. Effects of sirolimus and everolimus on fibroblast proliferation were also assessed. RESULTS: Mean adhesion score of the everolimus group (7.83 ± 1.17) was significantly lower compared to sirolimus (11.00 ± 0.63) and control (11.66 ± 0.51) groups. Mean collagen density of the everolimus group (33.5 ± 7.8) was significantly lower compared to sirolimus (50.7 ± 9.69) and control (53.8 ± 12.4) groups. Mean tensile strength of the control group (26.41 ± 2.10) was significantly higher compared to sirolimus (17.89 ± 1.9) and everolimus (21.37 ± 1.25) groups. It was significantly lower in sirolimus group than everolimus group. Both sirolimus and everolimus treated media inhibited fibroblast proliferation significantly compared to media alone. CONCLUSIONS: Everolimus effectively reduced adhesions. Nevertheless, it also reduced wound tensile strength: an effect which seemed to be due to inhibition of fibroblast proliferation.
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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) | 0.000 | 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) | 0.001 | 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".