Prevention of postoperative peritoneal adhesions by O-carboxymethyl chitosan in a rat cecal abrasion model
Bibliographic record
Abstract
PURPOSE: Post-surgical adhesion formation can result in significant morbidity and mortality. N,O-carboxymethyl chitosan (N,O-CMC) has been previously shown to be effective in the prevention of postsurgical adhesion formation. In this study, we evaluated the ability of O-carboxymethyl chitosan (O-CMC), another chitosan derivative generated by carboxymethylation of chitosan's oxygen centers, to reduce postsurgical adhesion development. METHODS: Twenty male Sprague-Dawley rats (250 +/- 20 g) were divided into two equal groups: O-CMC group and saline (control) group. All rats underwent a midline laparotomy and the cecum was abraded to cause petechial hemorrhages. Following peritoneal injections of either saline or O-CMC, the incisions were closed. Seven days after surgery, the animals were killed and adhesion formation was scored. Tissue samples from the adhesions were examined histochemically. RESULTS: Adhesion formation was significantly decreased in the O-CMC group (P < .001) in comparison with the control group. Furthermore, significantly less collagen (P < .001) and fewer inflammatory cells and fibroblasts were detected in the O-CMC-treated animals. Additionally, a significantly (P < .05) lower level of TGF-beta1 expression was found in the O-CMC group. CONCLUSION: O-CMC appears to be effective in the prevention of postoperative peritoneal adhesion formation, which may be attributed to decreased accumulation of inflammatory cells and fibroblasts and reduced collagen synthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".