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Nance May posted an update 6 months, 1 week ago
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. 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-instance deep learning classifiers built on images in both the sagittal and transverse views with a mean pooling operation obtained an AUC of 0.961±0.026 with a classification rate of 0.925±0.060, specificity of 0.986±0.032, and sensitivity of 0.873±0.120, respectively. Discriminative regions of the kidney located using classification activation map demonstrated that the deep learning techniques could identify meaningful anatomical features from ultrasound images. Conclusion The multi-instance deep learning method provides an automatic and accurate means to extract informative features from ultrasound images and discriminate infants with PUV from male children with unilateral hydronephrosis.The heterogenous nature of high-risk non-muscle invasive bladder cancer encompasses a wide range of tumor biologies with varying recurrence and progression risks. Radical cystectomy provides excellent oncologic outcomes but is often underutilized. Timing for these patients is critical, however, to its effectiveness. Certain unfavorable tumor characteristics predict worse outcomes and may help select the most appropriate patients for more aggressive initial therapy. This manuscript aims to outline factors that predict worse outcomes in high-risk non-muscle invasive bladder cancer and proposes which patients may benefit most from a timely radical cystectomy.Urologic and gynecologic surgeons are the top utilizers of robotic surgery; however, non-obstetrical robotic-assisted laparoscopic surgery (RALS) in pregnant patients is infrequent. A systematic literature review was performed to ascertain the frequency, indication and complications of RALS in pregnancy. Results showed thirty-eight pregnancies from eleven publications between 2008-2020. Five cases were for urologic indication and thirty-three for gynecologic indication. Minimal surgical alterations were required. Although no adverse maternal-fetal outcomes were reported, there are not enough cases published to determine safety. This review demonstrates the feasibility of RALS for the pregnant population in the hands of competent robotic surgeons.Objective To study disease-specific knowledge and decisional quality in men with varicocele being counseled for infertility. Methods A instrument designed to measure decisional quality by evaluating disease-specific knowledge, decisional conflict, and impression that shared decision-making was administered to 92 men identified to have a varicocele seeking their initial infertility consultation. Mean scores on disease-specific knowledge questionnaire, prevalence of decisional conflict, and impact of consultation on preferred infertility treatment were analyzed. Results 55% of patients were found to have decisional conflict. selleck Compared to those with decisional conflict, men without decisional conflict scored higher on the infertility knowledge assessment (63% vs 53% correct) and were more likely to feel that they discussed treatment options with their physician in detail (98% vs 82%) (all p less then 0.01). Prior to consultation, 28% of all patients preferred assisted reproductive technologies and 2% preferred varicocelectomy as the primary treatment for infertility.
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