Sheridanzhao3337
Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens.
Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms.
Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 73, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI 0.81, 0.87), respectively.
Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.
Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.
During the Coronavirus Disease 2019 (COVID-19) pandemic, emergency departments and fever clinics nurses acted as gatekeepers to the health care system. To manage the psychological problems that these nurses experience, we should develop appropriate training and intervention programs.
To identify the impact of COVID-19 on the psychology of Chinese nurses in emergency departments and fever clinics and to identify associated factors.
This online cross-sectional study recruited participants through snowball sampling between 13 February and 20 February 2020. Nurses self-administered the online questionnaires, including a general information questionnaire, the Self-Rating Anxiety Scale, the Perceived Stress Scale-14, and the Simplified Coping Style Questionnaire.
We obtained 481 responses, of which 453 were valid, an effective response rate of 94.18%. find more Participants who had the following characteristics had more mental health problems female gender, fear of infection among family members, regretting being a nurse, less rest time, more night shifts, having children, lack of confidence in fighting transmission, not having emergency protection training, and negative professional attitude.
Effective measures are necessary to preserve mental health of nurses in emergency departments and fever clinics. These include strengthening protective training, reducing night shifts, ensuring adequate rest time, and timely updating the latest pandemic situation.
Effective measures are necessary to preserve mental health of nurses in emergency departments and fever clinics. These include strengthening protective training, reducing night shifts, ensuring adequate rest time, and timely updating the latest pandemic situation.
Individuals with chronic conditions require ongoing disease management to reduce risks of adverse health outcomes. During the COVID-19 pandemic, health care for non-COVID-19 cases was affected due to the reallocation of resources towards urgent care for COVID-19 patients, resulting in inadequate ongoing care for chronic conditions.
A keyword search was conducted in PubMed, Google Scholar, Science Direct, and Scopus for English language articles published between January 2020 and January 2021.
During the COVID-19 pandemic, in-person care for individuals with chronic conditions have decreased due to government restriction of elective and non-urgent healthcare visits, greater instilled fear over potential COVID-19 exposure during in-person visits, and higher utilization rates of telemedicine compared to the pre-COVID-19 period. Potential benefits of a virtual-care framework during the pandemic include more effective routine disease monitoring, improved patient satisfaction, and increased treatment compliane urgent need for better chronic disease management strategies moving forward.
Overall, this review elucidates the disproportionately greater barriers to primary and specialty care that patients with chronic diseases face during the COVID-19 pandemic and emphasizes the urgent need for better chronic disease management strategies moving forward.Selective mutism (SM) is a childhood disorder characterized by a consistent failure to speak in specific social situations (eg, school) despite speaking normally in other settings (eg, at home). This article summarizes evidence supporting the recent classification of SM as an anxiety disorder and discusses the implications of this re-classification for the assessment and treatment of SM in clinical practice. Meanwhile, clinicians should also realize that SM sometimes is a heterogeneous disorder in which other problems are also present that complicate the management of children with SM. As examples, we discuss speech and language problems, developmental delay, and autism spectrum disorders.
Previous studies have shown that people always pay more attention to highly preferred items of choice, which is well defined by behavioral measurements and eye-tracking. However, less is known about the neural dynamics underlying the role that visual attention plays in value-based decisions, especially in those characterized by the "relative value" (ie, value difference) between two items displayed simultaneously in a binary choice.
This study examined the neural temporal and neural oscillatory features underlying selective attention to subjective preferences in value-based decision making.
In this study, we recorded electroencephalography (EEG) measurements while participants performed a binary choice task in which they were instructed to respond to their preferred snack in high value difference (HVD) or low value difference (LVD) conditions.
Behaviorally, participants showed faster responses and lower error rates in the HVD condition than in the LVD condition. In parallel, participants exerted a reduced prefrontal N2 component and attenuated frontal theta-band synchronization in the HVD condition as opposed to the LVD condition.