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Record W2753739174 · doi:10.1016/j.stemcr.2017.07.024

Functional Enterospheres Derived In Vitro from Human Pluripotent Stem Cells

2017· article· en· W2753739174 on OpenAlexafffund
Rohan R. Nadkarni, Soumeya Abed, Brian Cox, Sonam Bhatia, Jennifer Lau, Michael G. Surette, Jonathan S. Draper

Bibliographic record

VenueStem Cell Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsPopulation Health Research InstituteUniversity of TorontoMcMaster University
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchNational Metal and Materials Technology CenterCanada Research Chairs
KeywordsBiologyInduced pluripotent stem cellIn vitroStem cellCell biologyComputational biologyGeneticsEmbryonic stem cellGene

Abstract

fetched live from OpenAlex

Intestinal organoids derived from human pluripotent stem cells (hPSCs) are valuable in vitro research models that enable simplified access to human gastrointestinal tissues. Here, we report the in vitro generation of enterospheres (hEnS) from hPSC-derived gastrointestinal epithelial precursors. hEnS are cystic spheroids with a simple uniform structure composed entirely of intestinal epithelium. hEnS express markers of mature brush border cells and share a transcriptome profile similar to that of more mature intestinal organoids. Modulation of signaling cues enables control of hEnS growth and differentiation, including long-term propagation. We show that hEnS can be exploited for functional studies: hEnS display an innate immune response when treated with enteric pathogens, and transgenic modification of hEnS with a fluorescence cell-cycle reporter enables hEnS-forming stem cell enrichment. Our work establishes hEnS as an accessible and tractable in vitro modeling system for studying human gastrointestinal biology.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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".

Quick stats

Citations19
Published2017
Admission routes2
Has abstractyes

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