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Record W2903492705 · doi:10.1002/jcp.27794

α‐2‐Macroglobulin induces the shedding of microvesicles from cutaneous wound myofibroblasts

2018· article· en· W2903492705 on OpenAlexafffund
Alexandra Laberge, Akram Ayoub, Syrine Arif, Sébastien Larochelle, Alain Garnier, Véronique Moulin

Bibliographic record

VenueJournal of Cellular Physiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrovesiclesLRP1Cell biologyMyofibroblastWound healingStimulationReceptorBiogenesisMediatorChemistryEndocytosisScavenger receptorBiologyLDL receptorBiochemistryImmunologyLipoproteinEndocrinologyInternal medicinemicroRNACholesterolMedicineGeneFibrosis

Abstract

fetched live from OpenAlex

Microvesicles (MVs) are recognized as an important class of cell-to-cell messengers. Although the properties of MVs are increasingly documented, the mechanisms regulating MV biogenesis remain debated. Myofibroblasts are a key cellular component of wound healing and have been shown to produce MVs upon stimulation with serum. However, the mediator(s) responsible for the observed effect of serum on MV release have yet to be identified. To isolate the molecule(s) of interest, serum proteins were sequentially separated using chromatography, selective precipitation, and electrophoresis. MV production was assessed throughout the purification and after stimulation of myofibroblasts with two potent purified molecules. α-2-Macroglobulin (A2M) was thereby found to dose-dependently stimulate MV release. We confirmed the presence of the A2M receptor, low-density lipoprotein receptor-related protein-1 (LRP1), on myofibroblasts. Inhibition of LRP1 resulted in a significant decrease in MV production. Together, our results suggest that A2M positively regulates MV shedding through the activation of LRP1 on myofibroblasts.

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 score0.009
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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