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Record W2146377217 · doi:10.1139/y09-076

c-Ski, Smurf2, and Arkadia as regulators of TGF-β signaling: new targets for managing myofibroblast function and cardiac fibrosisThis article is one of a selection of papers published in a special issue celebrating the 125th anniversary of the Faculty of Medicine at the University of Manitoba.

2009· article· en· W2146377217 on OpenAlexafffundvenueabout
Ryan H. Cunnington, Mansoreh Nazari, Ian Dixon

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsSMADMediatorCardiac fibrosisTransforming growth factorExtracellular matrixCell biologyMyofibroblastTransforming growth factor betaReceptorSignal transductionFibrosisR-SMADBiologyCancer researchChemistryMedicineInternal medicineEndoglinStem cell

Abstract

fetched live from OpenAlex

Recent studies demonstrate the critical role of the extracellular matrix in the organization of parenchymal cells in the heart. Thus, an understanding of the modes of regulation of matrix production by cardiac myofibroblasts is essential. Transforming growth factor beta (TGF-beta) signaling is transduced through the canonical Smad pathway, and the involvement of this pathway in matrix synthesis and other processes requires precise control. Inhibition of Smad signaling may be achieved at the receptor level through the targeting of the TGF-beta type I receptors with an inhibitory Smad7/Smurf2 complex, or at the transcriptional level through c-Ski/receptor-Smad/co-mediator Smad4 interactions. Conversely, Arkadia protein intensifies TGF-beta-induced effects by marking c-Ski and inhibitory Smad7 for destruction. The study of these TGF-beta mediators is essential for future treatment of fibrotic disease, and this review highlights recent relevant findings that may impact our understanding of cardiac fibrosis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations39
Published2009
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicCardiac Fibrosis and RemodelingFrench-language works237,207