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Mohd Azhar Mohd Yasin

Researcher at Universiti Sains Malaysia

Publications -  57
Citations -  2104

Mohd Azhar Mohd Yasin is an academic researcher from Universiti Sains Malaysia. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 12, co-authored 41 publications receiving 1462 citations. Previous affiliations of Mohd Azhar Mohd Yasin include Monash University.

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Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study

Mahshid Dehghan, +355 more
- 04 Nov 2017 - 
TL;DR: High carbohydrate intake was associated with higher risk of total mortality, whereas total fat and individual types of fat were related to lower total mortality.
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Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study

Victoria Miller, +355 more
- 04 Nov 2017 - 
TL;DR: Higher total fruit, vegetable, and legume intake was inversely associated with major cardiovascular disease, myocardial infarction, cardiovascular mortality, non-cardiovascular mortality, and total mortality in the models adjusted for age, sex, and centre (random effect).
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Association of dietary nutrients with blood lipids and blood pressure in 18 countries: a cross-sectional analysis from the PURE study.

Andrew Mente, +427 more
TL;DR: Simulations suggest that ApoB-to-ApoA1 ratio probably provides the best overall indication of the effect of saturated fatty acids on cardiovascular disease risk among the markers tested, which is at odds with current recommendations to reduce total fat and saturated fats.
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Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)

TL;DR: A proposed machine learning (ML) scheme was tested and validated with resting-state EEG data involving 33 MDD patients and 30 healthy controls and proved suitable as clinical diagnostic tools for MDD.
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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

TL;DR: Significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.