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Record W2921364717 · doi:10.2741/4764

Effect of BMP-9 on endothelial cells and its role in atherosclerosis

2019· review· en· W2921364717 on OpenAlexaff
Nathalie Faucheux

Bibliographic record

VenueFrontiers in bioscience · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicBiomarkers in Disease Mechanisms
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsExtracellular matrixCell biologyFibronectinChemistryEndotheliumBasement membraneMechanotransductionEndothelial dysfunctionArteriosclerosisInternal medicineEndocrinologyMedicineBiology

Abstract

fetched live from OpenAlex

Atherosclerosis is an inflammatory disease involving dysfunction of endothelial cells (EC) and enhanced permeability of the endothelium to oxidized low-density lipoprotein and the transmigration of monocytes from the blood to the intima where they are transformed into foam cells after lipid engulfment. Changes in the composition of the basement membrane leading to increased fibronectin deposition also occur and modify EC-extracellular matrix (ECM) mechanotransduction. The release of lipids due to foam cell apoptosis, as well as the migration of vascular smooth muscle cells from the media to the intima and their proliferation, increase the stiffness of arteries at later stages of atherosclerosis. EC dysfunction also involves other factors, including soluble cytokines and growth factors (GF) such as bone morphogenetic proteins (BMP). BMP-9 is a potent circulatory GF which has been shown to affect EC behavior. However, to date, few studies have investigated its role in atherosclerosis. The present review describes the histology and homeostasis of arteries by explaining EC function/dysfunction and discusses BMP-9 effect on EC behavior, considering factors engaged in the development of atherosclerosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.265
Teacher spread0.250 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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