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Record W2168279238 · doi:10.1194/jlr.r600008-jlr200

Thematic review series: Patient-Oriented Research. What have we learned about HDL metabolism from kinetics studies in humans?

2006· review· en· W2168279238 on OpenAlexaff
Shirya Rashid, Bruce W. Patterson, Gary F. Lewis

Bibliographic record

VenueJournal of Lipid Research · 2006
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversity of TorontoMcGill University
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsThematic mapSeries (stratigraphy)KineticsComputational biologyComputer scienceBiologyGeographyCartographyPhysics

Abstract

fetched live from OpenAlex

Plasma measurements of lipids, lipoproteins, and apolipoproteins provide information on the static levels of these fractions without providing key information on the dynamic fluxes of lipoproteins in the circulation. Kinetics studies, in contrast, provide additional information on the production and clearance rates of lipoproteins and the flow of lipids and apolipoproteins through lipoprotein fractions. This information is crucial in accurately delineating the metabolism of HDL in plasma, because plasma concentrations of HDL are the net result of the de novo production and catabolism of HDL as well as the recycling of HDL particles and the contribution to HDL from components of other lipoproteins. Studies aimed at measuring the metabolism of HDL particles have shown that HDL metabolism in vivo is complex and consists of multiple components. Kinetics studies provide a window into the metabolism of HDL, allowing us to better understand the mechanisms of HDL decrease in human conditions and the functionality of HDL particles. Here, we review the progress in our understanding of HDL metabolism derived from in vivo kinetics studies, focusing primarily on studies in humans but also reviewing key studies in animal models.

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)0.020
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.003
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.007
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 head0.374
GPT teacher head0.516
Teacher spread0.141 · 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 designOther design
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

Citations28
Published2006
Admission routes1
Has abstractyes

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