A229 PROMOTION OF COLONIC ANASTOMOTIC HEALING WITH PERIOPERATIVE SUPPLEMENTATION WITH OLIGOSACCHARIDES
Bibliographic record
Abstract
Abstract Background Colorectal resection is a standard procedure in the management of colorectal cancer (CRC) and inflammatory bowel disease. Anastomotic leak (AL) is a major complication in colorectal resections, and the gut microbiota may play a role in the healing and development of AL. Short-chain fatty acids (SCFAs), namely butyrate, have been involved in anastomotic healing when administered into the bowel via enema. Due to the mechanical stress associated with enemas after the confection of a fresh and fragile anastomosis, other butyrate-increasing strategies are required. Aims To promote anastomotic healing and prevent AL by using inulin and galacto-oligosaccharides (GOS) supplementation to modulate the microbiota toward a butyrate-producing profile. Methods Mice were fed diets supplemented with inulin, GOS or cellulose, as a non-fermentable control, for two weeks and underwent a proximal colonic anastomosis under general anesthesia. Healing of the anastomosis, both macroscopically and microscopically, was assessed six days after surgery. Epithelial proliferation, mucus production and integrity of the gut barrier were assessed. Results Inulin and GOS supplementation increased SCFAs in the colon and were associated with better postoperative weight recovery and macroscopic anastomotic healing. Microscopically, mucosal continuity was promoted by inulin and GOS. Mucus production was found to be similar in all groups. The gut barrier was found to be improved with inulin and GOS as shown by less bacterial translocation. Conclusions Inulin and GOS may prevent AL and promote anastomotic healing. This effect appears to be mediated by improved mucosal proliferation. Funding Agencies CIHRNatural Sciences and Engineering Research Council of Canada; Institut du cancer de Montréal; Fonds de recherche du Québec en santé
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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) | 0.000 | 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) | 0.002 | 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".