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Record W2767843191 · doi:10.1111/1440-1681.12888

Dynamic changes of the composition of plasma<scp>HDL</scp>particles in patients with cardiac disease: Spotlight on sphingosine‐1‐phosphate/serum amyloid A ratio

2017· review· en· W2767843191 on OpenAlexaff
Emmanuel E. Egom, Haaris A. Shiwani, Rebabonye B. Pharithi, Rebecca E. A. Canning, Barkat Ullah Khan, Yassine El Hiani, Vincent Maher

Bibliographic record

VenueClinical and Experimental Pharmacology and Physiology · 2017
Typereview
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSphingosine-1-phosphateSphingolipidSerum amyloid AInternal medicineCholesterolInflammationLipoproteinMedicineEndocrinologySphingosineHigh-density lipoproteinDiseaseAmyloid (mycology)ChemistryBiochemistryPathologyReceptor

Abstract

fetched live from OpenAlex

Several epidemiological studies reported an inverse relationship between plasma high-density lipoprotein (HDL) cholesterol levels and atherosclerotic cardiovascular disease (ASCVD). However, therapeutic interventions targeted at raising HDL-cholesterol failed to improve cardiovascular outcomes, suggesting that HDL components distinct from cholesterol may account for the anti-atherothrombotic effects attributed to this lipoprotein. Sphingosine-1-phosphate (S1P) and the acute phase protein serum amyloid A (SAA) have been identified as integral constituents of HDL particles. Evidence suggests that S1P and SAA levels within HDL particles may be affected by inflammation and oxidative stress, which are coexisting processes underlying ASCVD. Because SAA, an inflammation-related marker, and S1P, an anti-atherothrombotic marker, have relatively clear opposite characteristics among the HDL-associated proteins, the approach of assessing the two markers simultaneously may provide new insights in clinical practice (S1P/SAA Index). This review focuses on evidence in support of the concept that the S1P/SAA Index may affect the HDL atheroprotective properties and may, therefore represent a potential target for therapeutic interventions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.383
Teacher spread0.347 · 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 designObservational
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

Citations4
Published2017
Admission routes1
Has abstractyes

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