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Record W2750757231 · doi:10.1111/1750-3841.13841

Phenolics from Winemaking By‐Products Better Decrease VLDL‐Cholesterol and Triacylglycerol Levels than Those of Red Wine in Wistar Rats

2017· article· en· W2750757231 on OpenAlexafffund
Walkia Polliana de Oliveira, A. C. T. Biasoto, Valquíria Fernanda Marques, Iêda Maria dos Santos, Kedma Magalhães, L. C. Corrêa, Melissa Negro‐Dellacqua, María Miranda, Adriano Costa de Camargo, Fereidoon Shahidi

Bibliographic record

VenueJournal of Food Science · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsMemorial University of Newfoundland
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorEmpresa Brasileira de Pesquisa AgropecuáriaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsWinemakingWineFood scienceChemistryNutraceuticalFlavonolsRaw materialPolyphenolBiochemistryAntioxidantOrganic chemistry

Abstract

fetched live from OpenAlex

Winemaking by-products account for more than 30% of the grape production, but this inexpensive feedstock has not yet been fully exploited. Accordingly, we evaluated the potential biological activity of winemaking by-products produced with Syrah grapes in comparison with those of the wine produced using the same grape cultivar. Winemaking by-products showed higher contents of total anthocyanins, flavonols, stilbenes, and flavanols than red wine as evaluated by HPLC-DAD-FD (on a dry weight basis). In contrast, red wine was a better source of phenolic acids. However, the contribution of phenolic acids was minor for both samples. Furthermore, equivalent concentration of winemaking by-products (100 mg/kg/d) showed greater biological activity by than that of red wine by decreasing the levels of VLDL-cholesterol and triacylglycerols in Wistar rats. Therefore, this study supports the use of winemaking by-products as an economical source of bioactive phenolics with potential use in the food and nutraceutical industries.

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)0.001
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 score0.213
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.316
Teacher spread0.264 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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