IDDF2022-ABS-0225 The GUT microbiota modulates colonic healing in patients undergoing surgery for colorectal cancer
Bibliographic record
Abstract
Background The standard of care of colorectal cancer (CRC) management consists of surgical resection of the colon or rectum, followed by a reconnection, or ‘anastomosis’, of the remaining bowel ends to re-establish gastrointestinal continuity. Up to 30% of patients may present poor healing of the anastomosis, and anastomotic leak (AL), a major complication that increases mortality and morbidity after surgery. Our objective is to investigate the possible role of the gut microbiome in anastomotic healing in patients with CRC. Methods Preoperative fecal samples were collected from CRC patients undergoing surgery. The gut microbiota of patients with AL and of others that presented optimal healing were analyzed and compared using the Anchor pipeline. Fecal microbiota transplantation (FMT) was performed in mice using preoperative fecal samples from CRC patients with and without AL. Mice were then subjected to colonic surgery using a colonic anastomosis model. After 6 days, anastomotic healing and the gut barrier were assessed. The gut microbiota composition was compared as well to detect potential differences between the groups of mice transplanted from donors with and without AL. Results Mice colonized by FMT with the microbiota of donors with AL displayed macroscopically poorer healing of the colonic anastomosis and a higher bacterial translocation to the spleen, suggestive of a weaker gut barrier after surgery. The anastomotic wounds of mice receiving the microbiota of AL donors displayed lower concentrations of collagen and fibronectin and higher inflammatory cytokines, indicating poor extracellular matrix formation after surgery. This was accompanied by a higher expression of collagenolytic enzymes, indicative of collagen degradation at the wound site. The beta diversity of the gut microbiota was significantly different between mice receiving the microbiota of donors with and without AL. Several bacterial species were differentially abundant between the two groups and were associated with the healing process. Conclusions The preoperative gut microbiota in CRC patients with poor postoperative healing induces poor healing in mice and a weaker gut barrier after surgery. These results suggest a causal role for the gut microbiota in colonic healing after surgery in patients with CRC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".