Gut microbiota influence anastomotic healing in colorectal cancer surgery through modulation of mucosal proinflammatory cytokines
Bibliographic record
Abstract
Objective Colorectal cancer (CRC) is the third most diagnosed cancer, and requires surgical resection and reconnection, or anastomosis, of the remaining bowel to re-establish intestinal continuity. Anastomotic leak (AL) is a major complication that increases mortality and cancer recurrence. Our objective is to assess the causal role of gut microbiota in anastomotic healing. Design The causal role of gut microbiota was assessed in a murine AL model receiving faecal microbiota transplantation (FMT) from patients with CRC collected before surgery and who later developed or not, AL. Anastomotic healing and gut barrier integrity were assessed after surgery. Bacterial candidates implicated in anastomotic healing were identified using 16S rRNA gene sequencing and were isolated from faecal samples to be tested both in vitro and in vivo . Results Mice receiving FMT from patients that developed AL displayed poor anastomotic healing. Profiling of gut microbiota of patients and mice after FMT revealed correlations between healing parameters and the relative abundance of Alistipes onderdonkii and Parabacteroides goldsteinii . Oral supplementation with A. onderdonkii resulted in a higher rate of leaks in mice, while gavage with P. goldsteinii improved healing by exerting an anti-inflammatory effect. Patients with AL and mice receiving FMT from AL patients presented upregulation of mucosal MIP-1α, MIP-2, MCP-1 and IL-17A/F before surgery. Retrospective analysis revealed that patients with AL present higher circulating neutrophil and monocyte counts before surgery. Conclusion Gut microbiota plays an important role in surgical colonic healing in patients with CRC. The impact of these findings may extend to a vast array of invasive gastrointestinal procedures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".