A76 MODULATING THE PREOPERATIVE GUT MICROBIOTA USING DIETARY FIBER TO IMPROVE COLORECTAL CANCER SURGICAL OUTCOMES
Bibliographic record
Abstract
Abstract Background Anastomotic leak (AL) is a postoperative complication that occurs in up to 20% of patients undergoing surgery for colorectal cancer (CRC). It is characterized by the poor healing of the intestinal reconnection and is associated with increased mortality, morbidity, and cancer recurrence. The gut microbiota plays a key role in anastomotic healing, potentially mediated by the production of beneficial short-chain fatty acids (SCFA). Supplementation with the dietary fiber inulin was shown to increase SCFA as well as improve microscopic and macroscopic anastomotic healing in a mouse surgical model. However, when considering clinical applications, differences in baseline microbiota composition and patient ability to respond to a dietary fiber must be taken into account. Aims In order to differentiate between responders and non-responders prior to surgery, we tested patient responses to different fibers in a mouse fecal microbiota transplantation (FMT) model. Our overall objective is to identify microbial or systemic markers that could predict patient response to different fibers, allowing for personalized dietary interventions. Methods Wild-type C57BL/6 mice received FMT from a human donor. Following a 2 week engraftment period, mice received supplementation with one of four dietary fibers for 2 weeks, after which fecal samples were collected for SCFA analysis using HPLC-MS. Results The microbiota response to each dietary fiber was estimated based on increased fecal SCFA levels at endpoint for each FMT donor. Conclusions Our FMT mouse model is able to detect increased SCFA levels in response to dietary fiber supplementation. Future validation will include measuring post-operative intestinal healing parameters in a mouse surgical model and comparing these results with an ongoing clinical trial. By lowering the risk of AL, we aim to decrease treatment burden for CRC patients and improve their quality of life post treatment. Funding Agencies CIHRFRQS
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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 | 0.001 | 0.001 |
| 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".