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Record W4206628421 · doi:10.1016/j.celrep.2021.110283

An in vitro chronic damage model impairs inflammatory and regenerative responses in human colonoid monolayers

2022· article· en· W4206628421 on OpenAlexafffund
William D. Rees, Nikita Telkar, David Lin, May Q. Wong, Chad Poloni, Ayda Fathi, Michael S. Kobor, Nicholas C. Zachos, Theodore S. Steiner

Bibliographic record

VenueCell Reports · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsBC Cancer AgencyBC Children's HospitalUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthCrohn's and Colitis CanadaMitacsCrohn's and Colitis Foundation of CanadaBC Children's Hospital
KeywordsIn vitroCell biologyInflammationRegeneration (biology)Regenerative medicineBiologyChemistryMedicineImmunologyComputational biologyStem cellBiochemistry

Abstract

fetched live from OpenAlex

Acute damage to the intestinal epithelium can be repaired via de-differentiation of mature intestinal epithelial cells (IECs) to a stem-like state, but there is a lack of knowledge on how intestinal stem cells function after chronic injury, such as in inflammatory bowel disease (IBD). We developed a chronic-injury model in human colonoid monolayers by repeated rounds of air-liquid interface and submerged culture. We use this model to understand how chronic intestinal damage affects the ability of IECs to (1) respond to microbial stimulation, using the Toll-like receptor 5 (TLR5) agonist FliC and (2) regenerate and protect the epithelium from further damage. Repeated rounds of damage impair the ability of IECs to regrow and respond to TLR stimulation. We also identify mRNA expression and DNA methylation changes in genes associated with IBD and colon cancer. This methodology results in a human model of recurrent IEC injury like that which occurs in IBD.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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 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

Citations16
Published2022
Admission routes2
Has abstractyes

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