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Effect of sugar cane policosanols on cholesterol metabolism and LDL oxidation in hypercholesterolemic individuals

2008· article· en· W2278044680 on OpenAlexaff
Amira Kassis, Stan Kubow, Peter J.H. Jones

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicNatural Products and Biological Research
Canadian institutionsUniversity of ManitobaMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsCholesterolChemistryCrossover studyPlaceboLipid oxidationLdl cholesterolSugarMetabolismAbsorption (acoustics)Internal medicineFood scienceEndocrinologyBiochemistryMedicineAntioxidant

Abstract

fetched live from OpenAlex

Sugar cane policosanols (SCP) purportedly exert cholesterol‐modulating properties including reductions in LDL oxidation and cholesterol synthesis, as shown by a Cuban research group. However, independent research examining LDL oxidation and cholesterol synthesis is limited to very few studies contradicting outcomes of original research. Moreover, no data are available on the effect of SCP on cholesterol absorption to date. Therefore, objectives of the present study were to examine the effect of Cuban SCP on LDL oxidation, and to determine the changes in cholesterol kinetics in hypercholesterolemic individuals. Twenty‐one subjects participated in a randomized double blind crossover where they consumed 10 mg/day of policosanols or placebo incorporated in margarine during 28 days. Cholesterol absorption and synthesis were measured using 13 C labelled cholesterol and deuterium water respectively, administered the last week of each phase. Plasma LDL oxidation was measured using a solid phase two site enzyme immunoassay. Results show no difference in endpoint oxidized LDL concentrations between SCP and placebo treatments. Similarly, absorption and synthesis rates did not differ between groups. Our results demonstrate that SCP fail to alter LDL oxidation status and cholesterol 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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.321
Teacher spread0.288 · 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 designBench or experimental
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

Citations0
Published2008
Admission routes1
Has abstractyes

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