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Beneficial Effects of Pomegranate Fruit Consumption in Cardiovascular Diseases Prevention

2019· article· en· W2913798399 on OpenAlexvenueno aff
L. Benchagra, Abdelouahed Hajjaji, Mhamed Ramchoun, Ahmed Bazil Bin Khalil, H. Berrougui

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2019
Typearticle
Languageen
FieldNursing
TopicPomegranate: compositions and health benefits
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Traditional medicineMedicinePharmacologyFood scienceChemistry

Abstract

fetched live from OpenAlex

Oxidative stress, dyslipidemia, hypercoagulability, endothelial dysfunction and inflammation are key elements in the development of atherosclerosis. Oxidative stress has been implicated as well in most of the key steps in the pathophysiology of atherosclerosis and the consequential clinical manifestations of cardiovascular diseases. In addition to the formation of atherosclerosis, oxidative stress acute thrombotic events, including dyslipidemia, the oxidation of low-density lipoproteins (LDLs) and plaque rupture leading to atherothrombosis and myocardial infarction. In the last decades, multiple experimental studies and clinical trials have demonstrated that diet plays a central role in the prevention of atherosclerosis. Pomegranate (Punica granatum L.) is one of nature’s most concentrated sources of antioxidants. It contains some very potent antioxidants (i.e. tannins, anthocyanins and flavonoids), which provide a wide spectrum of action against free radicals and are considered to be potent anti-atherogenic products. These properties make pomegranate a healthy fruit with a high potential in preventing cardiovascular diseases. In this review, we give an overview on the newest insights in the role of pomegranate in therapy of vascular 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.506

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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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