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Record W1595504708 · doi:10.5539/jfr.v4n4p38

Effects of Baru Almond and Brazil Nut Against Hyperlipidemia and Oxidative Stress In Vivo

2015· article· en· W1595504708 on OpenAlexvenueno aff
Daniela Canuto Fernandes, Aline Medeiros Alves, Gabriela Salim de Castro, Alceu Afonso Jordão, Maria Margareth Veloso Naves

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsnot available
FundersMinisterio de Economía y Competitividad
KeywordsBaruBrazil nutNutFood scienceLipid peroxidationMacadamia nutMalondialdehydeHyperlipidemiaChemistryAnimal scienceOxidative stressBiologyBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

This study evaluated the effect of baru (Dipteryx alata Vog.) almond, an edible seed native from Brazilian Savanna, and Brazil nut (Bertholletia excelsa H. B. K.) on serum lipid profile and hepatic lipid peroxidation in rats fed high-fat diets. Four groups of eight young adult male Wistar rats were treated for nine weeks with one of the following diets: high-fat diets - 0.1% colic acid + 1% cholesterol + 5% lard + 15% of lipid from lard, baru almond or Brazil nut - and reference diet (7% soybean oil). Groups fed with baru almond and Brazil nut showed lower serum contents of total cholesterol and triacylglycerols than those of lard group. Baru almond group also showed higher HDL-c concentration than those of Brazil nut and lard groups, similar to that of reference group. Lipid peroxidation (through total malondialdehyde) was lower and vitamin E content was higher in the livers of the animals treated with baru almond and Brazil nut than those of lard group. These results indicate that the Brazilian native oilseeds, especially baru almond, have great potential for dietary use in dyslipidemia prevention and control.

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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.044
GPT teacher head0.369
Teacher spread0.325 · 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

Citations44
Published2015
Admission routes1
Has abstractyes

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