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Record W4287010281 · doi:10.3390/cells11152272

Canadian Contributions in Fibroblast Biology

2022· review· en· W4287010281 on OpenAlexafffundabout
Danah S. Al‐Hattab, Sikta Chattopadhyaya, Michael P. Czubryt

Bibliographic record

VenueCells · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMyofibroblastFibroblastFibrosisCardiac fibrosisStromal cellBiologyPathologyPhenotypeCell biologyImmunologyMedicineCancer researchCell cultureGenetics

Abstract

fetched live from OpenAlex

Fibroblasts are stromal cells found in virtually every tissue and organ of the body. For many years, these cells were often considered to be secondary in functional importance to parenchymal cells. Over the past 2 decades, focused research into the roles of fibroblasts has revealed important roles for these cells in the homeostasis of healthy tissue, and has demonstrated that activation of fibroblasts to myofibroblasts is a key step in disease initiation and progression in many tissues, with fibrosis now recognized as not only an outcome of disease, but also a central contributor to tissue dysfunction, particularly in the heart and lungs. With a growing understanding of both fibroblast and myofibroblast heterogeneity, and the deciphering of the humoral and mechanical cues that impact the phenotype of these cells, fibroblast biology is rapidly becoming a major focus in biomedical research. In this review, we provide an overview of fibroblast and myofibroblast biology, particularly in the heart, and including a discussion of pathophysiological processes such as fibrosis and scarring. We then discuss the central role of Canadian researchers in moving this field forwards, particularly in cardiac fibrosis, and highlight some of the major contributions of these individuals to our understanding of fibroblast and myofibroblast biology in health and disease.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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.

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

Citations7
Published2022
Admission routes3
Has abstractyes

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