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Record W4405151349 · doi:10.1101/2024.12.05.24318563

Human Organ Chips Reveal New Inflammatory Bowel Disease Drivers

2024· preprint· en· W4405151349 on OpenAlexaff
Aliçan Özkan, Gwenn E. Merry, David B. Chou, Ryan R. Posey, Anna Stejskalová, Karina Calderon, Megan M. Sperry, Viktor Horváth, Lorenzo Ferri, Emanuela Carlotti, Stuart A.C. McDonald, Douglas J. Winton, Rocco Riccardi, Sean R. R. Hall, Girija Goyal, Donald E. Ingber

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsInflammatory bowel diseaseInflammationFibrosisMucusDiseaseStromaMedicineCystic fibrosisImmunologyCancer researchPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Inflammatory bowel disease (IBD) patients exhibit compromised intestinal barrier function and decreased mucus accumulation, as well as increased inflammation, fibrosis, and cancer risk, with symptoms often being exacerbated in women during pregnancy. Here, we show that these IBD hallmarks can be replicated using human Organ Chips lined by IBD patient-derived colon epithelial cells interfaced with matched fibroblasts cultured under flow. Use of heterotypic tissue recombinants revealed that IBD fibroblasts are the primary drivers of multiple IBD symptoms. Inflammation and fibrosis are accentuated by peristalsis-like motions in IBD Chips and when exposed to pregnancy-associated hormones in female IBD Chips. Carcinogen exposure also increases inflammation, gene mutations, and chromosome duplication in IBD Chips, but not in Healthy Chips. These data enabled by human Organ Chip technology suggest that the intestinal stroma, sex hormones, and peristalsis-associated mechanical deformations play a key role in driving inflammation, fibrosis, and disease progression in male and female IBD patients.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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