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Record W3094011249 · doi:10.3390/jcm9113390

Role of the Fatty Acid Binding Proteins in Cardiovascular Diseases: A Systematic Review

2020· review· en· W3094011249 on OpenAlexafffund
Hien C. Nguyen, Mohammad Qadura, Krishna K. Singh

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsUniversity of TorontoSt. Michael's HospitalWestern University
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineFatty acid-binding proteinComputational biologyBiochemistryBioinformaticsGene

Abstract

fetched live from OpenAlex

Cardiovascular diseases (CVD) remain a global pandemic and leading cause of deaths worldwide. While several guidelines have been developed to control the development of CVDs, its prevalence keeps on increasing until this day. Cardiovascular risk factors, such as reduced exercises and high fat or glucose diets, culminate in the development of the metabolic syndrome and eventually atherosclerosis, which is driven by high blood lipid and cholesterol levels, and by endothelial dysfunction. Late complications of atherosclerosis give rise to serious clinical cardiovascular manifestations such as myocardial infarction and hypertension. Therefore, endothelial functions and the lipid metabolism play critical roles in the pathogenesis of CVDs. Fatty acid-binding proteins are a family of intracellular proteins expressed in many cell types known mainly for their interaction with and trafficking of cellular lipids. The roles of a number of isoforms in this family have been implicated in lipid metabolic homeostasis, but their influence on endothelial function and vascular homeostasis remain largely unknown. This review's purpose is to update fundamentals about the connection between cardiovascular disease, metabolism, endothelial function, and mainly the roles of fatty acid-binding proteins.

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)0.003
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.295
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.373
Teacher spread0.333 · 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.

Study designSystematic review
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

Citations36
Published2020
Admission routes2
Has abstractyes

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