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Intestinal epithelial wound healing assay in an epithelialâmesenchymal co-culture system

2010· article· en· W2072512421 on OpenAlexafffund
Amira Seltana, Nùria Basora, Jean‐François Beaulieu

Bibliographic record

VenueWound Repair and Regeneration · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsWound healingBasement membraneEpitheliumMesenchymal stem cellMyofibroblastCell biologyFibronectinCell cultureChemistryBiologyPathologyExtracellular matrixImmunologyMedicineFibrosis

Abstract

fetched live from OpenAlex

Rapid and efficient healing of epithelial damage is critical to the functional integrity of the small intestine. Epithelial repair is a complex process that has largely been studied in cultured epithelium but to a much lesser extent in mucosa. We describe a novel method for the study of wound healing using a co-culture system that combined an intestinal epithelial Caco-2/15 cell monolayer cultured on top of human intestinal myofibroblasts, which together formed a basement membrane-like structure that contained many of the major components found at the epithelial-mesenchymal interface in the human intestine. To investigate the mechanism of restitution, small lesions were generated in epithelial cell monolayers on plastic or in co-cultures without disturbing the underlying mesenchymal layer. Monitoring of wound healing showed that repair was more efficient in Caco-2/15-myofibroblast co-cultures than in Caco-2/15 monolayers and involved the deposition of basement membrane components. Functional experiments showed that the addition of type I collagen or human fibronectin to the culture medium significantly accelerated wound closure on epithelial cell co-cultures. This system may provide a new tool to investigate the mechanisms that regulate wound healing in the intestinal epithelium.

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 score0.461
Threshold uncertainty score0.493

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 integrity0.0000.000
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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Citations36
Published2010
Admission routes2
Has abstractyes

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