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Introduction: The unpredictable challenges and conditions of COVID-19 can cause mental health problems. In such a situation, one of the most important psychological problems is the fear and anxiety of death. Death anxiety can affect the quality of patient care services and the job satisfaction and mental health of nurses. Methods: This is a descriptive cross-sectional study in which 110 nurses working in the intensive care units of hospitals affiliated to the Tehran University of Medical Sciences were selected by the convenience sampling method from April to September 2016. The data collection tools used in the study include a demographic questionnaire and a Templer death-anxiety questionnaire. Findings: The results showed that the level of death anxiety in nurses working at COVID-19 intensive care units is associated with age, working hours per week, childbearing, several patients needing end-of-life care, cases of direct participation in resuscitation operations, cases of patient death observations, and satisfaction with personal protective equipment (P < o.o5). Conclusion: Increasing the nurses' awareness of the critical situations of COVID-19, management measures, improving the working environment, social support, and increasing personal protective equipment seem to be the effective factors in protecting the intensive care unit nurses against COVID-19 and reducing death anxiety. [ABSTRACT FROM AUTHOR]

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Medical Signals & Sensors.)

Background: The recent outbreak of the coronavirus disease (COVID-19) in China has rapidly spread throughout the world and there are many reports of symptoms ranging from malaise to acute respiratory distress syndrome (ARDS) caused by this infection. However, few reports have been discussed surgical outcomes in COVID-19 patients., Case Presentation: In this report, we described a case of an elderly female developed with postoperative pulmonary complications after uneventful elective minor surgery. The patient was asymptomatic before the operation with no history of cough or fever. After surgery, the patient developed respiratory distress and chest radiological imaging revealed bilateral ground-glass opacities. It seems any type of surgeries requiring local anesthesia or general anesthesia may contribute to worsening outcomes in patients with covid19., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)