An umbrella review of meta-analysis to understand the effect of coffee consumption and the relationship between stroke, cardiovascular heart disease, and dementia among its global users.

Coffee has long been popular worldwide. The rise in lifestyle-related diseases such as cardiovascular disease, diabetes, stroke, dementia, and others has motivated coffee usage and illness prevalence studies. Some studies show coffee consumers are at risk for such diseases, whereas others show its active components protect them.

Relation of skinfold thickness with the serum lipids, glucose and blood pressure among Indian sedentary office workers.

Background: Serum glucose, cholesterol, triglycerides and high-density lipoproteins (HDL) are established cardiovascular disease (CVD) markers, however accessibility to these markers is less in individuals from low-middle income countries. The non-invasive CVD risk marker especially skinfold measured fat percentages are less explored for its relevance with established serum biochemistry markers.

Methods: A cross-sectional study was conducted in 70 sedentary office workers (aged 30-40 years) who were healthy.

Clinicopathological Spectrum of Diabetic and Non-diabetic Renal Lesions in Patients with Diabetes Mellitus: An Experience from A Tertiary Care Center.

Background & Objective: Diabetic patients often develop lesions called non-diabetic renal diseases (NDRD), whose prognostic and therapeutic implications vary from diabetic nephropathy (DN). Since early identification of NDRD is associated with a better prognosis, we aimed to understand its spectrum.

Methods: One hundred and thirty-four patients were included in a cross-sectional study.

Novel statistically equivalent signature-based hybrid feature selection and ensemble deep learning LSTM and GRU for chronic kidney disease classification.

Chronic kidney disease (CKD) involves numerous variables, but only a few significantly impact the classification task. The statistically equivalent signature (SES) method, inspired by constraint-based learning of Bayesian networks, is employed to identify essential features in CKD. Unlike conventional feature selection methods, which typically focus on a single set of features with the highest predictive potential, the SES method can identify multiple predictive feature subsets with similar performance.