1%) among the non-Ob/Gyn applicants.
 
  Ob/Gyn applicants and non-Ob/Gyn applicants differed in their assessment of Ob/Gyn rotations. It is crucial to provide medical training based on interns' needs to improve their skills for treating female patients.
 Ob/Gyn applicants and non-Ob/Gyn applicants differed in their assessment of Ob/Gyn rotations. It is crucial to provide medical training based on interns' needs to improve their skills for treating female patients.Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.Measuring the image focus is an important issue in Shape from Focus methods. Conventionally, the Sum of Modified Laplacian, Gray Level Variance (GLV), and Tenengrad techniques have been used frequently among various focus measure operators for estimating the focus levels in a sequence of images. However, they have various issues such as fixed window size and suboptimal focus quality. To solve these problems, a new focus measure operator based on the adaptive sum of weighted modified Laplacian is proposed. First, the adaptive window size selection algorithm based on the GLV is applied. Next, appropriate weights are assigned to the Modified Laplacian values in the image window based on the distance between the center pixel and neighboring pixels. Finally, the Weighted Modified Laplacian values in the image window are summed. Experimental results demonstrate the effectiveness of the proposed method.
  We piloted a hand hygiene (HH) project in a ward, focusing on World Health Organization moments 1 and 4. Our aim was to design highly reliable interventions to achieve >90% compliance.
 
  Baseline HH compliance was 57 and 67% for moments 1, 4, respectively, in 2015. After the pilot ward showed sustained improvement, we launched the 'HH bundle' throughout the hospital. This included (i) appointment of HH champions; (ii) verbal/visual bedside reminders; (iii) patient empowerment; (iv) hand moisturisers; (v) tagging near-empty handrub (HR) bottles. Other hospital-wide initiatives included (vi) Smartphone application for auditing; (vii) 'Speak up for Patient Safety' Campaign in 2017 for staff empowerment; (viii) making HH a key performance indicator.
 
  Overall HH compliance increased from a baseline median of 79.6-92.6% in end-2019. Moments 1 and 4 improved from 71 to 92.7% and from 77.6 to 93.2%, respectively. Combined HR and hand wash consumption increased from a baseline median of 82.6 ml/patient day (PD) to 109.2 mL/PD. Health-care-associated rotavirus infections decreased from a baseline median of 4.5 per 10 000 PDs to 1.5 per 10 000 PDs over time.
 
  The 'HH Bundle' of appointing HH champions, active reminders and feedback, patient education and empowerment, availability of hand moisturisers, tagging near-empty hand rub bottles together with hospital-wide initiatives including financial incentives and the 'Speak Up for Patient Safety' campaign successfully improved the overall HH compliance to >90%. These interventions were highly reliable, sustained over 4 years and also reduced health-care-associated rotavirus infection rates.
 90%. These interventions were highly reliable, sustained over 4 years and also reduced health-care-associated rotavirus infection rates.
  To explore the sleep quality among Chinese nurses and identify the association between night shift and sleep quality and health.
 
  Chinese nurses have many night shifts; the effect of it regarding nurses' sleep quality and health is still not being explored.
 
  This was a cross-sectional study.  https://www.selleckchem.com/products/BIBF1120.html  There were 3,206 nurse participants. The participants self-completed a sociodemographic questionnaire, the Pittsburgh Sleep Quality Index (PSQI) and the Cornell Medical Index (CMI).
 
  Night shift nurses demonstrated relatively worse sleep quality (55.1%) and more health problems (20.7%). Night shift work was significantly associated with poor sleep quality (β=0.96, confidence interval [CI]=0.67-1.26) and poor health (β=2.01, CI=0.15-3.88). Except for sleep medication (β=0.02, CI=-0.01, 0.05) and psychological health (β=0.38, CI=-0.27, 1.03), night shift work was significantly associated with other PSQI domains and physical health.
 
  Night shift work was a risk factor for nurses' sleep quality and health. Night shift nurses have more sleep disorders and physical health problems.
 
  Nurse managers should pay attention to the impact of shift work on nurses' sleep quality and health and reform the rotating shift work system to improve nurses' occupational health.
 Nurse managers should pay attention to the impact of shift work on nurses' sleep quality and health and reform the rotating shift work system to improve nurses' occupational health.
  Malnutrition is prevalent in hospital, and the Subjective Global Assessment (SGA) has been widely used for its identification. However, in the last decade, new tools were proposed by the Academy of Nutrition and Dietetics-American Society for Parenteral and Enteral Nutrition (AND-ASPEN), European Society for Clinical Nutrition and Metabolism (ESPEN) and Global Leadership Initiative on Malnutrition (GLIM). The diagnostic test accuracy of these tools has been scarcely investigated. Thus, we aimed to compare the accuracy of AND-ASPEN, ESPEN and GLIM for malnutrition diagnosis in hospitalised patients.
 
  A cross-sectional study was conducted with hospitalised patients aged ≥18years from a five-unit complex hospital. Malnutrition was diagnosed within 48h of admission using SGA, AND-ASPEN, ESPEN and GLIM. The accuracy of these tools was evaluated by the area under the receiver operating characteristic (AUROC) curve, considering SGA as reference, which was compared by the DeLong test.
 
  Six hundred patients (55.7±14.