https://www.selleckchem.com/products/estradiol-benzoate.html This study assessed the perceived knowledge and competence, and the attitude of Saudi nursing students towards vital signs monitoring for detecting patient deterioration during clinical rotation. It also examined the predictors of students' attitudes. One of the most important uses of vital signs monitoring is the early detection of deterioration. Vital signs monitoring is one of the most frequently assigned tasks to students during clinical rotation. However, the attitudes of nursing students towards vital signs monitoring for detecting clinical deterioration remain unexplored. Quantitative, cross-sectional design. A convenience sample of 529 baccalaureate nursing students in two universities in Saudi Arabia was surveyed using the V-scale from October 2019-December 2019. A multivariate multiple regression was implemented to examine the multivariate effect of the predictor variables on the five subscales of the V-scale. This study adhered to the STROBE checklist. The overall attitudes of the studentd the physiological indicators of clinical deterioration. This study also identified areas that require improvement to ensure positive attitudes among students. To improve image quality and CT number accuracy of daily cone-beam computed tomography (CBCT) through a deep-learning methodology with Generative Adversarial Network. 150 paired pelvic CT and CBCT scans were used for model training and validation. An unsupervised deep-learning method, 2.5D pixel-to-pixel generative adversarial network (GAN) model with feature mapping was proposed. A total of 12000 slice pairs of CT and CBCT were used for model training, while 10-cross validation was applied to verify model robustness. Paired CT-CBCT scans from an additional 15 pelvic patients and 10 head-and-neck (HN) patients with CBCT images collected at a different machine were used for independent testing purpose. Besides the proposed method above, other network arch