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Quantitative analysis of small bowel epithelial gaps in IL-10 knockout and control 129 Sv/Ev mice

2009· article· en· W2320415679 on OpenAlexaff
Jingao Liu, P Boulanger, Karen Madsen, Richard N. Fedorak, Gordon Broderick, Jon Meddings

Bibliographic record

VenueInflammatory Bowel Diseases · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIleumColitisKnockout mouseCryptInflammatory bowel diseaseJejunumPathologyCecumIn vivoIntestinal mucosaBiologyImmunologyMedicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

The interleukin-10 knockout (IL-10 -/-) mouse is a homozygous IL-10 deficient strain generated on a 129 Sv/Ev genetic background. It is a well-established rodent IBD model that universally develop colitis after weaning at 8 weeks of age. Impaired barrier function has been implicated in the pathogenesis of inflammatory bowel disease. Confocal Endo-Microscopy (CEM) has demonstrated the existence of epithelial cell gaps resulting from cell shedding in the rodent model. We hypothesize that increased epithelial cell shedding, as measured by an increase in epithelial gaps, contributes to the abnormal intestinal permeability in the IL-10 -/- model. In this study, we measured the epithelial gaps normalized for epithelial cells in IL-10 -/- and compared to the background 129 Sv/Ev strain. We performed CEM on 8-week old IL-10 -/- mice (n= 15) and control 129 Sv/Ev mice (n= 10) using previously described methods. We confirmed the presence of minimal intestinal inflammation prior to the onset of colitis symptoms in the IL-10 -/- mice. We consistently imaged both the jejunum, at 10 cm distal to the ligament of Treitz, and ileum, at 10 cm proximal to the cecum, in each animal. In brief, segments of jejunum and ileum were exteriorized. The lumen was cut open the tissue was stained topically with 0.5 mmol/L with acriflavine hydrochloride, and then washed with saline. Cross-sectional images along the z-axis of the tissues (z-stacks) were obtained with the Five 1 Fluorescence in vivo confocal endomicroscopy system (Optiscan Ltd, Victoria, Australia). The cross-sectional images were aligned and the features extrapolated to create a surface relief of the small intestinal segment. Interpolation between the twodimensional (2-D) frames in each stack was performed using a feature-guided, shapebased computer method to adequately manage translation, rotation, and scaling. The three-dimensional (3-D) relief was analyzed to isolate and quantify the shape, extent, and position of the lesions created where cells had been shed (epithelial gap). Cellular features were analyzed from the 3-D reconstruction of cross-sectional images obtained from the rodent small intestine. Adequately imaged villi, defined as en face villi with 75% surface visualized in the CEM images, were used for the analysis. Ten villi from each animal were manually counted for epithelial cells and the number of epithelial gaps. The gap density function is defined as the number of epithelial gaps (gaps) divided by the number of intestinal epithelial cells (cells) counted in the 10 adequately imaged villi (gap density = gaps/cells). The mean number of epithelial cells per villi counted was 290 for control 129 Sv/Ev mice and 218 for the IL-10 -/- mice. The mean gap density (mean ± standard error) was 6.46±1.36 gaps/1000 cells for 129 Sv/Ev mice that were significantly lower than for the IL-10 -/- mice (17.40 ± 2.09 gaps/1000 cells; p<0.01). The epithelial gap density is significantly higher for IL-10 -/- mice compared to control 129 Sv/Ev mice. The significance of increased epithelial gaps in the pathogenesis of inflammatory bowel disease warrants further investigation.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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