https://www.selleckchem.com/products/sd49-7.html 67 ± 0.15 vs. 0.60 ± 0.14mm, p < 0.001; 1.07 ± 0.05 vs. 1.00 ± 0.14mm, p < 0.001; and 89.18 ± 66.22 vs. 37.74 ± 15.37, p < 0.001, respectively). There were mild-moderate positive relationships between Δ-BMI and the values of EFT and CIMT (r = 0.386, p = 0.007 and r = 0.314, p = 0.024, respectively). We also found weak linear relationships between Δ-BMI and Δ-endocan (r = 0.267, p = 0.036), and between Δ-EFT and Δ-CIMT (r 0.221; p 0.046). LSG can lead to reduction in the risk of cardiovascular disease by providing improvements in CIMT, EFT, and serum endocan values, which reflect early structural atherosclerotic changes in patients with severe obesity. LSG can lead to reduction in the risk of cardiovascular disease by providing improvements in CIMT, EFT, and serum endocan values, which reflect early structural atherosclerotic changes in patients with severe obesity.Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were carried out under defined clinical condition for the classification of sleep EEG. Subsequent upon the extraction of various features from the raw EEG data, neuro-fuzzy system is trained to classify the sleep stages into three major classes namely awake, slow wave sleep (SWS), and rapid eye movement sleep (REM). This classification would enable medical professionals to diagnose sleep related disorders accurately. The results obtained clearly indicate that the mean performance for SWS stage is profound as compared to REM and awake stage. Specificity and sensitivity of the proposed method are obtained as 95.4% and 80%, respectively. The average accuracy of the system employing neuro-fuzzy approach is found to be 90.6% in whic