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Record W2419774405 · doi:10.1139/apnm-2012-0416

Grape seed and skin extract alleviates high-fat diet-induced renal lipotoxicity and prevents copper depletion in rat

2013· article· en· W2419774405 on OpenAlexvenueno aff
Kamel Charradi, Salem Elkahoui, Ines Karkouch, Férid Limam, Ghaïth Hamdaoui, Fethy Ben Hassine, Michèle Véronique El May, Ahmed El May, Ezzedine Aouani

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
Fundersnot available
KeywordsLipotoxicityOxidative stressKidneyMedicineInternal medicineKidney diseaseEndocrinologyDiabetes mellitusTriglycerideGrape seed extractObesityRenal functionSteatosisCholesterolPathologyInsulin resistance

Abstract

fetched live from OpenAlex

Obesity is a public health problem that contributes to morbidity and mortality from diabetes, heart disease, stroke, and cancers. The purpose of this investigation was to analyse the link between obesity-induced oxidative stress, renal steatosis, and kidney dysfunction, as well as the protective effect of grape seed and skin extract. Rats were fed a standard diet or a high-fat diet for 6 weeks and were either treated or not treated with grape seed and skin extract. Fat-induced oxidative stress was evaluated in the kidney with a special emphasis on transition metals. High-fat diet induced triglyceride deposition and disturbances in kidney function parameters, which are linked to an oxidative stress status and depletion of copper from the kidney. Grape seed and skin extract abrogated almost all fat-induced kidney disturbances. Grape seed and skin extract exerted potential protection against fat-induced kidney lipotoxicity and should find potential application in other kidney-related diseases.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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