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Record W4413020806 · doi:10.1371/journal.pone.0328898

Development of a tissue-specific bioscaffold for intestinal stem cell culture

2025· article· en· W4413020806 on OpenAlexafffund
Sachin Kakar, Mathieu Derouet, Liyue Zhang, C. Gillis, Frederikke Larsen, Arghya Paul, Lauren E. Flynn, Samuel Asfaha

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsOccupational Cancer Research CentreWestern University
FundersNatural Sciences and Engineering Research Council of CanadaWestern UniversityCanadian Institutes of Health ResearchCancer Research Society
KeywordsStem cellBiologyCell biology

Abstract

fetched live from OpenAlex

The generation of a tissue-specific intestinal hydrogel derived from the native intestine has the potential to support and promote the growth of intestinal organoids. In this study, we aimed to develop hydrogels derived exclusively from intestinal extracellular matrix (ECM) or composites comprised of intestinal ECM combined with alginate that allow for greater tuning of the hydrogel properties. A novel mouse intestinal decellularization protocol was developed and the ECM characterized. Our analyses demonstrate that our protocol effectively removed cellular and nuclear content while preserving key ECM components including collagens, glycosaminoglycans, fibronectin and laminin. When the decellularized small intestine (DSI) was used to generate hydrogels, the resulting ECM showed bioactivity as demonstrated by metabolic and pro-proliferative effects on NIH 3T3 murine fibroblasts. Importantly, our novel DSI hydrogels also supported murine intestinal and colonic organoid growth similar to Matrigel® controls. These studies demonstrate that murine tissue-specific DSI hydrogels can provide a supportive environment for the culture of intestinal and colonic organoids in vitro.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.345

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 head0.054
GPT teacher head0.261
Teacher spread0.207 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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