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Record W4323353286 · doi:10.1093/jcag/gwac036.266

A266 DIET-BASED THERAPIES FOR INTESTINAL DYSFUNCTION INDUCED BY CLOSTRIDIOIDES DIFFICILE INFECTION

2023· article· en· W4323353286 on OpenAlexaff
Z Saqib, X Bai, Y Nishihara, J Lu, G De Palma, P Bercik, S Collins

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsylliumClostridioidesGut floraGastroenterologyMotilityShort-chain fatty acidInternal medicineFecesConstipationBiologyMedicineImmunologyMicrobiologyButyrateFood scienceDietary fiber

Abstract

fetched live from OpenAlex

Abstract Background Chronic gut dysfunction occurs in up to 25% of patients following antibiotic-treated C. difficile infection (CDI). We developed a humanized mouse model in which germ free mice colonized with microbiota from patients with severe constipation post-CDI developed slow colonic transit, as a result of damage to the Interstitial Cells of Cajal (ICC) network by pro-inflammatory macrophages. Colonic transit, immune activation and the ICC network normalized after fecal microbiota transplantation using samples from healthy mice, as well after treatment with psyllium fiber. Here we explored the long-term effects of psyllium and evaluated the therapeutic potential of pectin and quercetin in this model. Purpose 1) To investigate the time course of the beneficial effect of psyllium on colonic motility. 2) To explore possible therapeutic properties of flavonoids and pectin. Method Germ-free mice were colonized with microbiota from the post-CDI (PCDI) patient or healthy controls (HC). After 3 weeks, the mice were fed for 4-5 weeks with a control diet or diets with 15% psyllium (PSY), 10% pectin (PCT) or 0.05% quercetin (QCT). To evaluate time course of PSY on motility, control diet was administered for 3-weeks following PSY treatment. The bead expulsion test was used to assess colonic motility. Stool samples were collected for microbial profiling, and short and branched-chain fatty acids (SCFA/BCFA) analysis. Macrophages morphology and counts, and ICC network structure were evaluated by immunohistochemistry. Result(s) Compared to HC microbiota, colonization with post-CDI microbiota induced slow colonic transit in recipient mice, and this was normalized by PSY (n=13; p=0.02). The benefit of PSY was transient as colonic transit slowed following discontinuation of PSY (p=0.001). The changes in colonic transit were paralleled by switch in macrophages phenotype and damage to the ICC network. Additionally, discontinuation of PSY resulted in a return of microbial diversity (p< 0.001), SCFA/BCFA levels (acetic and propionic acid/ iso-butyric and valeric acid) and specific bacterial species abundances, to values seen in untreated mice colonized with post-CDI microbiota. Microbial analysis predicted potential pathways involved in macrophage polarization, including the synthesis of SCFA/BCFA, degradation of inositol and production of acetylglucosamine. PCT also normalized slow intestinal transit in mice colonized with post-CDI microbiota (p=0.003), restored phenotype of infiltrating macrophages, and improved the structural integrity of the ICC network. In contrast, QCT failed to improve gut dysfunction in PCDI mice. Conclusion(s) Our results suggest that the beneficial effects of psyllium in this model are transient. Dietary pectin, but not quercetin, may also serve as a novel treatment strategy to restore colonic motility and immune homeostasis in humans with severe constipation post-CDI. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; W. Garfield Weston Foundation 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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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
Published2023
Admission routes1
Has abstractyes

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