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Background: During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals. Methods: The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals. Results: In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died. Conclusion: Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate. [ABSTRACT FROM AUTHOR]

Instructions were given to the colleagues to see their I WhatsApp i to learn about wisdom quotes and search the details of the scientist posted in Wikipedia. Dear Editor, The importance of mastering emotional intelligence at the workplace has been emphasized more recently.[1] This paper discusses a personal reflection about a simplified psychological model tested using wisdom quotes to accomplish psychological well-being for sound mental health among the health professionals in our institution during the coronavirus disease (COVID-19) scenario. [Extracted from the article]

Background: Sleep is vital for every aspect of human life. Inadequate sleep has a massive negative impact on health and work. There is very limited information about the impact of COVID-19 on the sleep disturbance of health-care workers. In our current study, we aim to find answers to certain questions about the impact of the COVID-19 pandemic on sleep patterns on nurses working in COVID care area. Methods: A cross-sectional study was conducted on 305 health-care workers who were purposively enrolled for this study. The study was conducted at the Postgraduate Institute of Medical Education and Research, Chandigarh, India, from April to June 2021. An e-survey method was used to collect data. A questionnaire including sociodemographic characteristics, queries about sleep disturbances, and the patient health questionnaire-9 scale to assess anxiety among participants are among the research tools. Results: Mean age of health-care workers was 26.3 years (SD = 6.3). Most of them were women (81%) with a bachelor's degree in nursing (62%), nursing interns (46%), and married (71%). The majority of nurses (85%) were not infected with COVID and were given suitable personal protective equipment (46%) in the hospital. The majority of participants (45%) got 6–8 h of sleep per night did not receive any sleep therapy (90%). The most of participants (42%) reported that they did not enjoy performing activities and were under moderate stress (15.4). Conclusion: Health-care workers are struggling to cope with the COVID-19 pandemic with limited and almost hackneyed resources. Healthy sleep is everyone's right. The current situation of the pandemic has a great impact on the psychological health of frontline health-care workers by affecting their professional performance. [ABSTRACT FROM AUTHOR]

Information on people living with HIV (PLHIV) and current COVID-19 pandemic is still scarce in Indian setting. This case series of PLHIV with COVID-19 describes clinical characteristics and outcome in this special group of patients. This case series included 11 confirmed cases of COVID-19 among PLHIV admitted at a tertiary care hospital in Gujarat, India during April–December 2020. This retrospective study was conducted by doing secondary data analysis from case records of patients for various variables including demographic, clinical characteristics, HIV-related parameters, and outcome (discharged/death). The mean age of patients was 39.2 years ranging from 20 to 55 years. Nearly, 18% (2/11) of patients had major comorbidities like diabetes and hypertension. All were taking antiretroviral therapy drugs with >95% drug adherence and had CD4 count ranging from 79/cu.mm. to 1189/cu.mm. Majority (91%) of patients recovered and were discharged while only one patient (9%) died during course of COVID-19 treatment. COVID-19 showed a similar clinical and epidemiological profile among PLHIV like other group of people. Further studies with large-sample size are recommended to find risks of COVID-19 among PLHIV and its impact on treatment outcomes. [ABSTRACT FROM AUTHOR]