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Record W2056455295 · doi:10.1021/jf201079h

Anthocyanin Extract from Black Rice Significantly Ameliorates Platelet Hyperactivity and Hypertriglyceridemia in Dyslipidemic Rats Induced by High Fat Diets

2011· article· en· W2056455295 on OpenAlexafffund
Yan Yang, Marc Andrews, Yan Hu, Dongliang Wang, Yu Qin, Yanna Zhu, Heyu Ni, Wenhua Ling

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2011
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of TorontoCanadian Blood Services
FundersCanadian Blood Services
KeywordsHypertriglyceridemiaAnthocyaninBlack riceFood scienceTraditional medicineChemistryInternal medicineMedicineBiologyTriglycerideCholesterol

Abstract

fetched live from OpenAlex

Our previous studies have demonstrated that anthocyanin extract from black rice (AEBR) inhibits atherosclerosis. Whether dietary AEBR supplementation can affect platelet function, an important factor in the pathogenesis of cardiovascular diseases, remains unclear. The aim of the present study is to explore the effects and mechanisms of dietary AEBR supplementation on platelet function and lipid profile in dyslipidemic rats. We demonstrated herein that thromboxane A(2), the thrombogenic ratio of thromboxane A₂ and prostacyclin, serum calmodulin, and soluble P-selectin were significantly decreased in rats fed a high fat diet supplemented with AEBR. AEBR supplementation also remarkably lowered serum triglyceride and raised hepatic CPT-1 mRNA expression. These findings suggest that dietary intake of AEBR reduces platelet hyperactivity, hypertriglyceridemia, and body weight gain, and facilitates in the maintenance of optimal platelet function in dyslipidemic rats induced by high fat diets.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.229
Teacher spread0.208 · 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 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

Citations80
Published2011
Admission routes2
Has abstractyes

Explore more

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