Cardiovascular risk reduction in older people with type 2 diabetes mellitus-a comprehensive narrative review.

Metabolic targets are controversial in older people with type 2 diabetes due to functional heterogeneity and morbidity burden. Tight blood pressure and metabolic control appears beneficial in fit individuals who are newly diagnosed with type 2 diabetes and have fewer comorbidities. The benefits of low blood pressure and tight metabolic control is attenuated with the development of comorbidities, especially frailty.

Biomarkers for the diagnosis and treatment of rheumatoid arthritis - a systematic review.

Background: Rheumatoid arthritis (RA) is an autoimmune disease, symmetrically affecting the small joints. Biomarkers are tools that can be used in the diagnosis and monitoring of RA.

Aim: To systematically explore the role of the biomarkers: C-reactive protein (CRP), rheumatoid factor (RF), anti-cyclic citrullinated protein (Anti-CCP), 14-3-3η protein, and the multi-biomarker disease activity (MBDA) score for the diagnosis and treatment of RA.

The Role of 14-3-3 η as a Biomarker in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic multisystem inflammatory disorder with significant morbidity and mortality. Making an early diagnosis and providing appropriate treatment decisions based on clinical and other parameter results in good disease control. Biomarkers, such as C reactive protein (CRP), anti-cyclic citrullinated peptides (anti-CCP), and erythrocyte sedimentation rate (ESR), have been traditionally used.

Sarcopenic obesity as a determinant of cardiovascular disease risk in older people: a systematic review.

Background: Aging is associated with body composition changes that include a reduction of muscle mass or sarcopenia and an increase in visceral obesity. Thus, aging involves a muscle-fat imbalance with a shift toward more fat and less muscle. Therefore, sarcopenic obesity, defined as a combination of sarcopenia and obesity, is a global health phenomenon due to the increased aging of the population combined with the increased epidemic of obesity.

Convolutional neural network for automated mass segmentation in mammography.

Background: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features.