Sexual Quality of Life in Men <60 Years Old after Coronary Bypass Surgery.

PURPOSE: To explore the effect of undergoing coronary artery bypass grafting on sexual quality of life as an integral part of patients' health-related quality of life. METHODS: This cross-sectional study included 265 men ages 18 to 60 years (median age, 55) who underwent coronary artery bypass grafting 1 to 5 years before the study. Standardized questionnaires were implemented to evaluate participant pre- and postoperative sexual quality of life and the quality of counseling provided to patients. RESULTS: Among the patients, 77% were in a steady relationship. The general health score was 5.5 ± 2.8 (mean ± standard deviation) preoperatively and 6 ± 2.2 at follow-up (P = .01). No sexual counseling was given to 83% and 77% of the patients pre- and postoperatively, respectively. The mean sexual satisfaction score dropped from 6.5 ± 2.6 preoperatively to 4.7 ± 3 postoperatively (P < .001). The decline in sexual intercourse frequency and masturbation frequency was significant (P < .001 and P = .006, respectively). Linear regression analysis showed that general health status (P = .008), higher-quality counseling (P = .027), and preoperative sexual quality of life (P < .001) correlated positively with sexual quality of life, whereas sternal pain (P < .001), erectile dysfunction (P < .001), and fear of excessive cardiac burden (P < .001) correlated negatively. CONCLUSIONS: Middle-aged men experience decreased sexual quality of life after coronary artery bypass grafting. Preoperative sexual quality of life, general health, and higher-quality counseling positively affect postoperative sexual quality of life, whereas sternal pain, fear, and erectile dysfunction play a negative role. Pre- and postoperative care guidelines should be improved. Further prospective large cohort studies for males and females are required.

Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-Vs obtained from Y2O3-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.