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Record W3042005681 · doi:10.1242/jcs.227900

The myofibroblast at a glance

2020· review· en· W3042005681 on OpenAlexafffund
Pardis Pakshir, Nina Noskovičová, Monika Lodyga, Dong Ok Son, Ronen Schuster, Amanda Goodwin, Henna Karvonen, Boris Hinz

Bibliographic record

VenueJournal of Cell Science · 2020
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchMitacsMedical Research CouncilNational Institute for Health and Care Research
KeywordsMyofibroblastBiologyExtracellular matrixFibrosisGranulation tissueCell biologyContext (archaeology)Wound healingCell typeCellImmunologyPathologyMedicineGenetics

Abstract

fetched live from OpenAlex

" (contraction) in rat wound granulation tissue and, accordingly, named these cells 'myofibroblasts'. Now, myofibroblasts are not only recognized for their physiological role in tissue repair but also as cells that are key in promoting the development of fibrosis in all organs. In this Cell Science at a Glance and the accompanying poster, we provide an overview of the current understanding of central aspects of myofibroblast biology, such as their definition, activation from different precursors, the involved signaling pathways and most widely used models to study their function. Myofibroblasts will be placed into context with their extracellular matrix and with other cell types communicating in the fibrotic environment. Furthermore, the challenges and strategies to target myofibroblasts in anti-fibrotic therapies are summarized to emphasize their crucial role in disease progression.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.019
GPT teacher head0.315
Teacher spread0.296 · 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

Citations291
Published2020
Admission routes2
Has abstractyes

Explore more

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