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Record W2558016691 · doi:10.1007/s12576-016-0504-x

Canola oil rich in oleic acid improves diastolic heart function in diet-induced obese rats

2016· article· en· W2558016691 on OpenAlexafffund
Sijo Joseph Thandapilly, Pema Raj, Xavier Lieben Louis, Danielle Perera, Prasanga Yamanagedara, Peter Zahradka, Carla G. Taylor, Thomas Netticadan

Bibliographic record

VenueThe Journal of Physiological Sciences · 2016
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsResearch ManitobaSt. Boniface HospitalUniversity of ManitobaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaHospital Research Foundation
KeywordsCanolaOleic acidInternal medicineEndocrinologyDiastoleObesityCardiac function curveMedicineHeart failurePolyunsaturated fatty acidDiastolic functionFatty acidChemistryFood scienceBiochemistryBlood pressure

Abstract

fetched live from OpenAlex

Obesity is a leading cause of cardiovascular disease. It directly affects heart structure and function and contributes to heart failure. Diet is a major factor involved in the development of obesity along with genetic factors. We examined the effects of monounsaturated and polyunsaturated fatty acid-rich oils on cardiac structure and function in the diet-induced rodent model of obesity (DIO). Obese prone (OP) rats were fed a high-fat diet (HF; 55% of kcal) for 12 weeks; Sprague-Dawley rats fed commercial chow served as control. Echocardiography was performed to assess the cardiac structure and function in all rats at 12 weeks. OP rats fed the HF diet showed significant impairment in diastolic function compared to control rats. The HF diet containing high oleic canola oil significantly improved diastolic function of OP rats compared to the HF diet with lard. In conclusion, canola oil rich in oleic acid, when incorporated into an HF diet, prevents the development of diastolic dysfunction in DIO rats.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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 head0.063
GPT teacher head0.340
Teacher spread0.277 · 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

Citations47
Published2016
Admission routes2
Has abstractyes

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