MétaCan
Menu
← Back to cohort
Record W4323351282 · doi:10.1093/jcag/gwac036.047

A47 THE GUT MICROBIOTA INFLUENCES COLONIC HEALING AFTER SURGERY IN PATIENTS UNDERGOING BOWEL RESECTION FOR COLORECTAL CANCER

2023· article· en· W4323351282 on OpenAlexaffabout
Roy Hajjar, Emilia Gonzalez, G Fragoso, M Oliero, A A Alaoui, A Calvé, Hervé Vennin Rendos, S Djediai, T Cuisiniere, P Laplante, C Gerkins, A S Ajayi, K Diop, N Taleb, S Thérien, F Schampaert, Hefzi Alratrout, F Dagbert, R Loungnarath, H Sebajang, F Schwenter, R Wassef, R Ratelle, E Debroux, J -F Cailhier, B Routy, B Annabi, Nicholas J. B. Brereton, C Richard, M M Santos

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsAnastomosisColorectal cancerMedicineGut floraColorectal surgeryGastroenterologyComplicationSurgeryWound healingMicrobiomeFecesInternal medicineCancerAbdominal surgeryBioinformaticsImmunologyBiology

Abstract

fetched live from OpenAlex

Abstract Background The standard treatment of colorectal cancer (CRC) consists of a surgical resection of the colonic segment with the tumor, followed by a reconnection of the remaining bowel ends, or "anastomosis". The anastomosis may fail to heal in up to 20% of patients, which leads to anastomotic leak, a major complication that increases postoperative morbidity and mortality. This complication is unpredictable and its causes remain poorly understood. Purpose The objective of this study is to investigate the possible role of the gut microbiome in anastomotic healing after surgery in patients with CRC. Method We collected preoperative fecal samples and intraoperative mucosal samples from CRC patients undergoing surgery with anastomosis. The gut microbiota of patients with AL and of others that presented optimal healing after surgery was analyzed and compared using the Anchor 16S pipeline. To assess the role of the patients' microbiota in healing, 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. Six days after surgery, anastomotic healing was assessed macroscopically and microscopically. The gut barrier function was also assessed. The gut microbiota composition was compared between the groups colonized with samples from patients with and without AL to detect potential differences. Result(s) Mice colonized by FMT with the microbiota of donors with AL displayed poor anastomotic healing macroscopically, and a weaker wound microscopically. These same mice displayed a weaker gut barrier, as objectified by higher bacterial translocation to the spleen. The anastomoses of mice receiving the microbiota of AL donors displayed lower concentrations of collagen and fibronectin and higher inflammatory cytokines and collagenolytic enzymes, indicating poor extracellular matrix formation and collagen degradation locally.The beta-diversity of the gut microbiota was significantly different between mice receiving the microbiota of donors with and without AL, and several bacterial species were differentially abundant between the two groups. Conclusion(s) The preoperative gut microbiota in CRC patients who experience anastomotic leak after surgery induces poor anastomotic healing in mice and a weaker gut barrier after colonic surgery. Several bacterial species were found to be associated with the healing process. Please acknowledge all funding agencies by checking the applicable boxes below CIHR, Other Please indicate your source of funding; NSERC, FRQS, New Frontiers in Research, Montreal Cancer Institute. Disclosure of Interest None Declared

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.246
Teacher spread0.238 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of Gastroenterology→Same topicGut microbiota and health→French-language works237,207→