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Record W2968976791 · doi:10.3390/jcdd6030028

Cardiac Fibroblast to Myofibroblast Phenotype Conversion—An Unexploited Therapeutic Target

2019· review· en· W2968976791 on OpenAlexafffund
Michael P. Czubryt

Bibliographic record

VenueJournal of Cardiovascular Development and Disease · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsMyofibroblastFibrosisExtracellular matrixCardiac fibrosisFibroblastPeriostinPhenotypeCell biologyHeart failureCancer researchBiologyMedicinePathologyInternal medicineIn vitroGenetics

Abstract

fetched live from OpenAlex

Fibrosis occurs when the synthesis of extracellular matrix outpaces its degradation, and over time can negatively impact tissue and organ function. In the case of cardiac fibrosis, contraction and relaxation of the heart can be impaired to the point of precipitating heart failure, while at the same time fibrosis can result in arrhythmias due to altered electrical properties of the myocardium. The critical event in the evolution of cardiac fibrosis is the phenotype conversion of cardiac fibroblasts to their overly-active counterparts, myofibroblasts: cells demarked by their expression of novel markers such as periostin, by their gain of contractile activity, and by their pronounced and prolonged increase in the production of extracellular matrix components such as collagens. The phenotype change is dramatic, and can be triggered by many stimuli, including mechanical force, inflammatory cytokines, and growth factors. This review will explore fibroblast to myofibroblast transition mechanisms and will consider the therapeutic potential of targeting this process as a means to arrest or even reverse 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.287
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations48
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Development and DiseaseSame topicCardiac Fibrosis and RemodelingFrench-language works237,207