MétaCan
Menu
Back to cohort

Role of Microparticles in Cardiovascular Disease: Implications for Endothelial Dysfunction, Thrombosis, and Inflammation

2021· review· en· W3161133291 on OpenAlexaff
Leslie Marisol Lugo‐Gavidia, Dylan Burger, Vance B. Matthews, Janis M. Nolde, Márcio Galindo Kiuchi, Revathy Carnagarin, Dennis Kannenkeril, Justine Chan, Anu Joyson, Lakshini Y. Herat, Omar Azzam, Markus P. Schlaich

Bibliographic record

VenueHypertension · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsInflammationEndotheliumThrombosisPlateletDiseaseEndothelial dysfunctionPathophysiologyMedicineEndothelial stem cellPathologicalVascular diseaseMicroparticleImmunologyBioinformaticsPathologyBiologyCardiologyInternal medicineIn vitro

Abstract

fetched live from OpenAlex

Microparticles are small cell vesicles that are derived from the cell membrane in response to different biological processes. There is growing evidence supporting the association between microparticles and cardiovascular disease, as their pathophysiology commonly includes endothelial damage and chronic inflammation which also promote a prothrombotic state. The direct causal link between the release of the different subtypes of microparticles and their implications on physiological and pathological conditions is still not completely elucidated. However, evidence suggests microparticles released from platelets, leukocytes, and endothelium may help to evaluate vascular health as they have a relevant role in inflammation, endothelial function, and thrombosis. This review aims to provide a short overview of the biogenesis, characteristics, and detection methodology of microparticles with a special focus on their possible implication in cardiovascular settings.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.287
Teacher spread0.251 · 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

Citations62
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHypertensionSame topicExtracellular vesicles in diseaseFrench-language works237,207