MétaCan
Menu
← Back to cohort
Record W4294535328 · doi:10.1136/gutjnl-2022-iddf.72

IDDF2022-ABS-0225 The GUT microbiota modulates colonic healing in patients undergoing surgery for colorectal cancer

2022· article· en· W4294535328 on OpenAlexaff
Roy Hajjar, Emmanuel González, Gabriela Fragoso, Manon Oliero, Ahmed Amine Alaoui, Annie Calvé, Hervé Vennin Rendos, Souad Djediai, Thibault Cuisiniere, Patrick Laplante, Claire Gerkins, Ayodeji S. Ajayi, Khoudia Diop, Nassima Taleb, Sophie Thérien, Frédéricke Schampaert, Hefzi Alratrout, François Dagbert, Rasmy Loungnarath, Herawaty Sebajang, Frank Schwenter, Ramsès Wassef, Richard Ratelle, Éric Debroux, Jean‐François Cailhier, Bertrand Routy, Borhane Annabi, Nicholas J. B. Brereton, Carole Richard, Manuela M. Santos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalMcGill Genome CentreMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsColorectal cancerMedicineGut floraCancerGastroenterologyCancer researchInternal medicineImmunology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0420.008

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicColorectal Cancer Surgical Treatments→French-language works237,207→