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Record W1997777049 · doi:10.1002/jcp.22268

Shedding of microparticles by myofibroblasts as mediator of cellular cross‐talk during normal wound healing

2010· article· en· W1997777049 on OpenAlexafffund
Véronique Moulin, Dominique Mayrand, Hugo Messier, Maria Carmen Martínez, Carlos A. Lopez‐Vallé, Hervé Genest

Bibliographic record

VenueJournal of Cellular Physiology · 2010
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsCégep de ChicoutimiUniversité LavalCentre hospitalier universitaire de Québec
FundersCanadian Institutes of Health Research
KeywordsMyofibroblastWound healingFibroblastMesenchymal stem cellAngiogenesisCell biologyMediatorChemistryCellCell growthCancer researchBiologyImmunologyPathologyIn vitroMedicineFibrosisBiochemistry

Abstract

fetched live from OpenAlex

Interactions between cells are a crucial mechanism to correctly heal a wounded tissue. Myofibroblasts have a central role during healing but their means to communicate with other cells is unknown. Microparticles (MP) have demonstrated a potential role as mediators of cellular interactions during various diseases. We have analyzed the production of MP by normal (Wmyo) and pathological (hypertrophic scar, Hmyo) myofibroblasts and human dermal fibroblasts (Fb) when treated with serum or plasma as examples of body fluids. We have shown that the presence of these body fluids induced a very significant increase in MP production by Wmyo while no MP production was denoted for Hmyo and Fb. These effects were at least due to thermally sensitive protein(s) with a molecular mass >30 kDa. Furthermore, the increase in MP production was not linked to an increase in apoptotic Wmyo. MP characterization showed that VEGF and FGF2 were present in MP and that endothelial and (myo)fibroblast cell growth can be stimulated by MP treatment. We postulated that MP production by myofibroblasts could modulate mesenchymal cell growth and angiogenesis during normal healing.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.610

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 integrity0.0000.000
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 head0.005
GPT teacher head0.250
Teacher spread0.246 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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