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Record W2599907508 · doi:10.1161/res.113.suppl_1.a011

Abstract 011: Transcriptional Basis Of Epicardial Progenitor Cell Activation During Heart Development, Regeneration And Repair

2013· article· en· W2599907508 on OpenAlexaff
Guo N. Huang, Jeffrey E. Thatcher, John McAnally, Yongli Kong, Xiaoxia Qi, Wei Tan, J. Michael DiMaio, James F. Amatruda, Robert D. Gerard, Joseph A. Hill, Rhonda Bassel‐Duby, Eric N. Olson

Bibliographic record

VenueCirculation Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsStuart Olson (Canada)
Fundersnot available
KeywordsProgenitor cellParacrine signallingHeart developmentEnhancerBiologyCell biologyRegeneration (biology)Transcription factorProgenitorEmbryonic stem cellStem cellGeneGenetics

Abstract

fetched live from OpenAlex

The epicardium encapsulates the heart and functions as a source of multipotent progenitor cells and paracrine factors essential for cardiac development and repair. Injury of the adult heart results in reactivation of epicardial progenitor cells, which reengages a developmental gene program, but the underlying transcriptional basis has not been delineated. We established a mouse embryonic heart organ culture and gene expression system that facilitated the identification of epicardial enhancers activated during heart development and injury. Epicardial activation of these enhancers depends on a combinatorial transcriptional code centered on C/EBP, HOX, MEIS, and GRAINYHEAD families of transcription factors. Furthermore, disruption of C/EBP signaling in the adult epicardium reduced injury-induced neutrophil infiltration and improved cardiac function. These findings reveal a transcriptional basis for epicardial progenitor cell activation during heart development and injury, providing a platform for enhancing cardioprotection and regeneration.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.315
Teacher spread0.274 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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