Cardiovascular diseases (CVD) remain a major cause of death and morbidity globally and diet plays a crucial role in the disease prevention and pathology. The negative perception of dairy fats stems from the effort to reduce dietary saturated fatty acid (SFA) intake due to their association with increased cholesterol levels upon consumption and the increased risk of CVD development. Institutions that set dietary guidelines have approached dairy products with negative bias and used poor scientific data in the past. As a result, the consumption of dairy products was considered detrimental to our cardiovascular health. In western societies, dietary trends indicate that generally there is a reduction of full-fat dairy product consumption and increased low-fat dairy consumption. However, recent research and meta-analyses have demonstrated the benefits of full-fat dairy consumption, based on higher bioavailability of high-value nutrients and anti-inflammatory properties. In this review, the relationship between dairy consumption, cardiometabolic risk factors and the incidence of cardiovascular diseases are discussed. Functional dairy foods and the health implications of dairy alternatives are also considered. In general, evidence suggests that milk has a neutral effect on cardiovascular outcomes but fermented dairy products, such as yoghurt, kefir and cheese may have a positive or neutral effect. Particular focus is placed on the effects of the lipid content on cardiovascular health.

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SummaryAnaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker-controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.

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