Zimmerman Lauritzen (vacuumorder9)

pression and dementia might be complex and determined by multiple factors. As the Novel Corona Virus Disease (COVID-19) was declared by the world health organization a pandemic in March 2020, thousands of healthcare workers (HCWs) worldwide were on the frontlines fighting against the pandemic. Herein, we selected two Middle East countries; Egypt and Saudi Arabia to investigate the psychological impacts of the COVID-19 pandemic on their HCWs. In this cross-sectional study, a Google survey was used to access HCWs in many hospitals in Egypt and Saudi Arabia between the 14th and 24th of April 2020. The survey assessed HCWs regarding their sociodemographic and occupational features, sleeping hours, and psychological impacts of the COVID-19 pandemic using the Depression Anxiety Stress Scale-21 (DASS-21). This study included 426 HCWs (48.4% physicians, 24.2% nurses, and 27.4% other HCWs). Of them, 69% had depression, 58.9% had anxiety, 55.9% had stress, and 37.3% had inadequate sleeping (<6h/day). Female sex, age ≤30 years, working in Egypt, attending emergency and night shifts, watching/reading COVID-19 news ≥2h/day, and not getting emotional support from family, society, and hospital were associated with a high likelihood of depression, anxiety, stress, and inadequate sleeping. the cross-sectional design restricted our ability to distinguish between preexisting and emerging psychological symptoms. HCWs on the frontlines in Egypt and Saudi Arabia experienced depression, anxiety, stress, and inadequate sleeping during the COVID-19 pandemic. HCWs on the frontlines in Egypt and Saudi Arabia experienced depression, anxiety, stress, and inadequate sleeping during the COVID-19 pandemic.In this study an experimental rig is developed to investigate the influence of tissue constraint and cyclic loading on cell alignment and active cell force generation in uniaxial and biaxial engineered tissues constructs. Addition of contractile cells to collagen hydrogels dramatically increases the measured forces in uniaxial and biaxial constructs under dynamic loading. This increase in measured force is due to active cell contractility, as is evident from the decreased force after treatment with cytochalasin D. Prior to dynamic loading, cells are highly aligned in uniaxially constrained tissues but are uniformly distributed in biaxially constrained tissues, demonstrating the importance of tissue constraints on cell alignment. Dynamic uniaxial stretching resulted in a slight increase in cell alignment in the centre of the tissue, whereas dynamic biaxial stretching had no significant effect on cell alignment. Our active modelling framework accurately predicts our experimental trends and suggests that a slightly higher (3%) total SF formation occurs at the centre of a biaxial tissue compared to the uniaxial tissue. However, high alignment of SFs and lateral compaction in the case of the uniaxially constrained tissue results in a significantly higher (75%) actively generated cell contractile stress, compared to the biaxially constrained tissue. These findings have significant implications for engineering of contractile tissue constructs.CDSS (Clinical Decision Support System) is a domain within digital health that aims at supporting clinicians by suggesting the most probable diagnosis based on knowledge obtained from patient data. Usually, decision models used by current CDSS are static, i.e., they are not updated when new data are included, which could allow them to acquire new knowledge and enhance system accuracy. This paper proposes a dynamic decision model that automatically updates itself from classifier models using supervised machine learning algorithms. Our supervised learning process ranks several decision models using classifier performance measures, considering available patient data, filled by the health center, or local clinical guidelines. The decision model with the best performa