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Record W2071175410 · doi:10.1002/ibd.20763

Bacterial Overgrowth and Inflammation of Small Intestine After Carboxymethylcellulose Ingestion in Genetically Susceptible Mice

2008· article· en· W2071175410 on OpenAlexaff
Alexander Swidsinski, Victoria Ung, Beate C. Sydora, Vera Loening‐Baucke, Yvonne Döerffel, Hans Verstraelen, Richard N. Fedorak

Bibliographic record

VenueInflammatory Bowel Diseases · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBacteriaInflammationMucusMicrobiologyIngestionInflammatory bowel diseaseBiologyIntestinal mucosaDiseaseMedicineImmunologyPathologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Detergents and emulsifiers added to food may destroy the mucus barrier, which normally isolates bacteria from the intestinal wall, and lead to chronic bowel inflammation in susceptible persons. We investigated the influence of 2% carboxymethylcellulose (CMC) on the biostructure of the intestinal microbiota in IL-10 gene-deficient mice. METHODS: Twenty to 27-week-old IL-10 gene-deficient mice received either 2% CMC solution (n = 7) or water (n = 6) orally for 3 weeks. Intestinal bacteria were investigated using fluorescence in situ hybridization in paraffin-fixed sections of the intestine. RESULTS: CMC-treated IL-10 gene-deficient mice demonstrated a massive bacterial overgrowth, distention of spaces between villi, with bacteria filling these spaces, adherence of bacteria to the mucosa, and migration of bacteria to the bottom of the crypts of Lieberkuehn. Leukocytes migrated into the intestinal lumen in 4 of the 7 CMC mice. The changes were similar to those observed in Crohn's disease in humans and were absent in control animals. CONCLUSIONS: CMC induces bacterial overgrowth and small bowel inflammation in susceptible animals. Because of its ubiquity in products and its unrestricted use in food of the industrial world, CMC is an ideal suspect to account for the rise of IBD in the 20th century.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.609
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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 teacher head, 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

Citations164
Published2008
Admission routes1
Has abstractyes

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