Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods.

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs.

The role of vitamin D deficiency on COVID-19: a systematic review and meta-analysis of observational studies.

Objectives: Although vaccination has started, coronavirus disease 2019 (COVID-19) poses a continuing threat to public health. Therefore, in addition to vaccination, the use of supplements to support the immune system may be important. The purpose of this study was to synthesize evidence on the possible effect of low serum vitamin D levels (25[OH]D<20 ng/mL or 50 nmol/L) on COVID-19 infection and outcomes.

Histopathological evaluation of autopsy cases with isolated pulmonary fat embolism (IPFE): is cardiopulmonary resuscitation a main cause of death in IPFE?

Fat embolism (FE) may develop following many traumatic and atraumatic clinical conditions; however, fewer data exist regarding the occurrence of isolated pulmonary FE (IPFE). Cardiopulmonary resuscitation (CPR) is an emergency procedure for maintaining blood circulation and oxygenation during cardiac arrest. In this study, we aimed to evaluate the association of CPR with IPFE in autopsy cases.

Cytoprotective effects of molsidomine against methotrexate-induced hepatotoxicity: an experimental rat study.

Introduction And Aim: Methotrexate (Mtx) is an antineoplastic and immunosuppressive drug that may cause hepatotoxicity, whereas molsidomine (Mol) is a vasodilating and antioxidant agent. This study aimed to investigate the potential protective effects of Mol in Mtx-induced liver toxicity in rats.

Materials And Methods: Forty Wistar albino rats were equally divided into five groups: control, Mol, Mtx, Mol-Mtx, and Mtx-Mol.

A novel hybrid dimension reduction technique for undersized high dimensional gene expression data sets using information complexity criterion for cancer classification.

Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e.