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Record W1981248068 · doi:10.1007/s12079-009-0053-7

Pericytes display increased CCN2 expression upon culturing

2009· article· en· W1981248068 on OpenAlexafffund
Shiwen Xu, Vineeth Rajkumar, Christopher P. Denton, Andrew Leask, David Abraham

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
FundersScleroderma Society of OntarioArthritis Society
KeywordsCTGFFibroblastCell biologyFibronectinPericyteConnective tissueMyofibroblastExtracellular matrixWound healingBiologyPhenotypePopulationPathologyImmunologyCell cultureIn vitroGrowth factorFibrosisEndothelial stem cellMedicineGeneGenetics

Abstract

fetched live from OpenAlex

By providing a source of alpha-smooth muscle actin (alpha-SMA)-expressing myofibroblasts, microvascular pericytes contribute to the matrix remodeling that occurs during tissue repair. However, the extent to which pericytes may contribute to the fibroblast phenotype post-repair is unknown. In this report, we test whether pericytes isolated from human placenta can in principle become fibroblast-like. Pericytes were cultured in vitro for 11 passages. The Affymetrix mRNA expression profile of passage 2 and passage 11 pericytes was compared. The expression of type I collagen, thrombospondin and fibronectin mRNAs was induced by passaging pericytes in culture. This induction of a fibroblast phenotype was paralleled by induction of connective tissue growth factor (CTGF/CCN2) and type I collagen protein expression and the fibroblast marker ASO2. These results indicate that, in principle, pericytes have the capacity to become fibroblast-like and that pericytes may contribute to the population of fibroblasts in a healed wound.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations20
Published2009
Admission routes2
Has abstractyes

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