Timmons Beach (sweetsband01)

Aim To develop a protocol to ensure the quality of respiratory protective devices for healthcare workers nursing and treating patients with possible or confirmed COVID-19 in the Catharina hospital. Background Due to the COVID-19 outbreak a shortage of respirators is occurring worldwide; more specifically, CE-certified FFP2 respirators. This has resulted in an increased supply to hospitals of alternative respirators of uncertain quality. Nevertheless, the quality of the respirators used by our healthcare workers must be ensured. Method A protocol and criteria based on applicable standards was developed to ensure the quality of respirators. The protocol has been implemented at the Catharina hospital and includes verification of the documents accompanying the respirator, visual inspection of the respirator and a test for total inward leak of particles into respirators. Findings 67% of the respirators brands and types received in the Catharina hospital did not meet quality criteria. Metabolism inhibitor Conclusion With a simple verification protocol the quality of the respirators can be checked and guaranteed while there is a shortage of the CE approved respirators which are normally used. With this in-hospital protocol health care workers can be equipped with safe-to-use respirators.Emerging adulthood is a critical developmental period for examining food- and eating-related behaviors as long-term weight-related behavioral patterns are established. Virtual reality (VR) technology is a promising tool for basic and applied research on eating and food-related processes. Thus, the present study tested the validity and user perceptions of a highly immersive and realistic VR food buffet by (a) comparing participants' food selections made in the VR buffet and real-world (RW) food buffet cafeteria one-week apart, and (b) assessing participants' rated perceptions of their VR experience (0-100 scale). Participants comprised an ethnically diverse sample of emerging adults (N = 35, Mage = 20.49, SD = 2.17). Results revealed that participants' food selections in the VR and RW food buffets were significantly and positively correlated in Kcals, grams, carbohydrates, and protein (all p's less then 0.05). Moreover, participants perceived that (1) the VR buffet was natural (M = 70.97, SD = 20.92), (2) their lunch selection in the VR buffet represented a lunch they would select on an average day (M = 84.11, SD = 15.92); and (3) their selection represented a lunch they would select if the same foods were available (M = 91.29, SD = 11.00). Our findings demonstrated the validity and acceptability of our highly immersive and realistic VR buffet for assessing food selection that is generalizable to RW food settings one-week apart without precisely matched foods. The findings of this study support the utility of VR as a validated tool for research on psychological and behavioral food-related processes and training interventions among young adults.Objective To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis. Methods We built a robust pattern classifier to distinguish 86 children with PUV from 71 children with mild unilateral hydronephrosis based on ultrasound images (3504 in sagittal view and 2558 in transverse view) obtained during routine clinical care. Results The multi-instance deep learning classifier performed better than classifiers built on either single sagittal images or single transverse images. Particularly, the deep learning classifiers built on single images in the sagittal view and single images in the transverse view obtained area under the receiver operating characteristic curve (AUC) values of 0.796±0.064 and 0.815±0.071, respectively. AUC values of the multi-instance deep learning classifiers built on images in the sagittal and transverse views with mean pooling operation were 0.949±0.035 and 0.954±0.033, respectively. The multi