MétaCan
Menu
Back to cohort
Record W2135901138 · doi:10.1194/jlr.m400287-jlr200

Apolipoprotein A-I, A-II, and VLDL-B-100 metabolism in men

2004· article· en· W2135901138 on OpenAlexaff
Sophie Desroches, Marie‐Ève Paradis, Mélanie Pérusse, W. Roodly Archer, Jean Bergeron, Patrick Couture, Nathalie Bergeron, Benoı̂t Lamarche

Bibliographic record

VenueJournal of Lipid Research · 2004
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsApolipoprotein BVery low-density lipoproteinInternal medicineEndocrinologyMetabolismCatabolismLipid metabolismChemistryLipoproteinBiologyMedicineCholesterol

Abstract

fetched live from OpenAlex

The impact of a low-fat diet and a high-MUFA diet on apolipoprotein A-I (apoA-I), apoA-II, and VLDL-apoB-100 metabolism in conditions of unrestricted (ad libitum) energy intake was compared in 65 men randomly assigned to one of two predefined experimental diets. A subsample of 18 men participated in the kinetic study. Before and after the 6-7 week dietary intervention, kinetic subjects received a primed-constant infusion of [5,5,5-2H3]L-leucine for 12 h under feeding conditions. ApoA-I production rate (PR; -31.5%; P <0.001) and fractional catabolic rate (FCR; -24.3%; P <0.05) were significantly decreased after the low-fat diet. These changes in apoA-I PR and FCR with the low-fat diet were also significantly different from those observed with the high-MUFA diet (P <0.01 and P <0.05, respectively). ApoA-II FCR was significantly increased in the high-MUFA group only. No significant within- or between-diet difference was found in VLDL-apoB-100 PR or FCR. These results emphasize the differential impact of the low-fat diet and high-MUFA diet on HDL metabolism.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.046
GPT teacher head0.353
Teacher spread0.307 · 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 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

Citations31
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Lipid ResearchSame topicDiabetes, Cardiovascular Risks, and LipoproteinsFrench-language works237,207