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https://www.selleckchem.com/products/D-Cycloserine.html Background and objectives There has been a recent increase in older patients admitted to general hospitals. A significant percentage of hospitalized older patients are ≥75 years old, which differ from the patients aged 65 to 74 years old in terms of functional status at patient discharge. This study aims to compare sociodemographic, clinical features, and factors associated with length of hospital stay in youngest-old and oldest-old populations of inpatients referred to the consultation liaison psychiatry unit. Material and methods This is an observational, cross-sectional, retrospective, and comparative study. We obtained data from a sample of 1017 patients (≥65 years) admitted to a general hospital and referred from different services (medicine, surgery, etc.) to the consultation liaison psychiatry unit. The sample was divided into two groups of patients youngest-old (65-74 years) and oldest-old (≥75 years). Psychiatric evaluations were performed while the patients were on wards at the hospital. Psychopharmnical features. The time to referral to consultation liaison psychiatry unit seems to be a relevant factor associated with length of hospital stay.Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and,

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