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Record W1496935185 · doi:10.4137/dti.s447

Therapies to Increase ApoA-I and HDL-Cholesterol Levels

2008· article· en· W1496935185 on OpenAlexaff
Fabrizio S. Chiacchia

Bibliographic record

VenueDrug Target Insights · 2008
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsResverlogix (Canada)
Fundersnot available
KeywordsReverse cholesterol transportCholesterolMedicineInternal medicineEndocrinologyLipoproteinHigh-density lipoproteinExcretionEndogeny

Abstract

fetched live from OpenAlex

Cholesterol is transported around the body in the form of lipoprotein (lipid/protein) complexes, because it is almost insoluble in water. High-density lipoprotein (HDL) particles transport cholesterol from tissues back to the liver for excretion. Epidemiological studies have shown an inverse relationship between blood levels of HDL-cholesterol (HDL-c) and the incidence of clinically signifi cant atherosclerosis. The benefi cial effects of HDL in altering atherosclerotic disease are believed to involve elevated levels of HDL enhancing the effl ux of cholesterol from arterial walls, increasing transport of cholesterol from arteries to the liver for excretion. This reverse cholesterol transport (RCT) pathway is used to explain both HDL's role in lipid metabolism and the inverse association between HDL-c plasma concentration and the risk of cardiovascular disease. Based on the RCT model, ApoA-I is an attractive target for therapeutic intervention. Experimental manipulations to increase production of ApoA-I have been associated with reduced atherogenicity. There is a continuing need for novel therapies that increase the biosynthesis of HDL, to inhibit the progression of and even bring about regression of atherosclerosis. Small molecule compounds that increase the production of endogenous ApoA-I would be attractive therapeutic agents for treating dyslipidemias.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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 designOther design
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

Citations5
Published2008
Admission routes1
Has abstractyes

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