MétaCan
Menu
Back to cohort
Record W3033703090 · doi:10.1089/scd.2020.0075

Genetic Mouse Models and Induced Pluripotent Stem Cells for Studying Tracheal-Esophageal Separation and Esophageal Development

2020· article· en· W3033703090 on OpenAlexaff
Suleen Raad, Anu David, Jianwen Que, Christophe Fauré

Bibliographic record

VenueStem Cells and Development · 2020
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsForegutBiologyAtresiaEsophagusInduced pluripotent stem cellTracheoesophageal fistulaStem cellAnatomyEndodermCell biologyPathologyEmbryonic stem cellGeneticsMedicine

Abstract

fetched live from OpenAlex

Esophagus and trachea arise from a common origin, the anterior foregut tube. The compartmentalization process of the foregut into the esophagus and trachea is still poorly understood. Esophageal atresia/tracheoesophageal fistula (EA/TEF) is one of the most common gastrointestinal congenital defects with an incidence rate of 1 in 2,500 births. EA/TEF is linked to the disruption of the compartmentalization process of the foregut tube. In EA/TEF patients, other organ anomalies and disorders have also been reported. Over the last two decades, animal models have shown the involvement of multiple signaling pathways and transcription factors in the development of the esophagus and trachea. Use of induced pluripotent stem cells (iPSCs) to understand organogenesis has been a valuable tool for mimicking gastrointestinal and respiratory organs. This review focuses on the signaling mechanisms involved in esophageal development and the use of iPSCs to model and understand it.

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)0.001
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.003

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 head0.064
GPT teacher head0.272
Teacher spread0.208 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueStem Cells and DevelopmentSame topicEsophageal and GI PathologyFrench-language works237,207