ABSTRACT
Background and Objectives: Due to the unexpected spread of coronavirus disease 2019 (COVID-19), there was a serious crisis of emergency medical system collapse. Healthcare workers working in the emergency department were faced with psychosocial stress and workload changes. Materials and Methods: This was a cross-sectional survey of healthcare workers in the emergency department in Daegu and Gyeongbuk, Korea, from November 16 to 25, 2020. In the survey, we assessed the general characteristics of the respondents; changes in the working conditions before and after the COVID-19 pandemic; and resulting post-traumatic stress disorder, depression and anxiety statuses using 49 questions. Results: A total of 529 responses were collected, and 520 responses were included for the final analyses. Changes in working conditions and other factors due to COVID-19 varied by emergency department level, region and disease group. Working hours, intensity, role changes, depression and anxiety scores were higher in the higher level emergency department. Isolation ward insufficiency and the risk of infection felt by healthcare workers tended to increase in the lower level emergency department. Treatment and transfer delay were higher in the fever and respiratory disease groups (M = 3.58, SD = 1.18; M = 4.08, SD = 0.95), respectively. In all the disease groups, both treatment and transfer were delayed more in Gyeongbuk than in Daegu. Conclusions: Different goals should be pursued by the levels and region of the emergency department to overcome the effects of the COVID-19 pandemic and promote optimal care.
Subject(s)
COVID-19 , Emergency Medical Services , Anxiety , Cross-Sectional Studies , Depression/epidemiology , Disease Outbreaks , Emergency Service, Hospital , Health Personnel , Humans , Pandemics , SARS-CoV-2 , Stress, Psychological/epidemiology , WorkloadABSTRACT
The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians in chest radiography (CR) diagnoses with Coronavirus disease 2019 (COVID-19) pneumonia, the automatic interpretation of the CR with DL algorithms can significantly reduce the burden on clinicians and radiologists in sudden surges of suspected COVID-19 patients. The aim of this study was to evaluate the efficacy of the DL algorithm for detecting COVID-19 pneumonia on CR compared with formal radiology reports. This is a retrospective study of adult patients that were diagnosed as positive COVID-19 cases based on the reverse transcription polymerase chain reaction among all the patients who were admitted to five emergency departments and one community treatment center in Korea from February 18, 2020 to May 1, 2020. The CR images were evaluated with a publicly available DL algorithm. For reference, CR images without chest computed tomography (CT) scans classified as positive for COVID-19 pneumonia were used given that the radiologist identified ground-glass opacity, consolidation, or other infiltration in retrospectively reviewed CR images. Patients with evidence of pneumonia on chest CT scans were also classified as COVID-19 pneumonia positive outcomes. The overall sensitivity and specificity of the DL algorithm for detecting COVID-19 pneumonia on CR were 95.6%, and 88.7%, respectively. The area under the curve value of the DL algorithm for the detection of COVID-19 with pneumonia was 0.921. The DL algorithm demonstrated a satisfactory diagnostic performance comparable with that of formal radiology reports in the CR-based diagnosis of pneumonia in COVID-19 patients. The DL algorithm may offer fast and reliable examinations that can facilitate patient screening and isolation decisions, which can reduce the medical staff workload during COVID-19 pandemic situations.
Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic/methods , SARS-CoV-2/genetics , Triage/methods , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/virology , Data Accuracy , Female , Humans , Male , Middle Aged , Radiologists , Reproducibility of Results , Republic of Korea/epidemiology , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , Sensitivity and Specificity , Tomography, X-Ray Computed/methodsABSTRACT
A EED pseudogene transcript (psXEED) has been isolated from the cDNA pool of Xenopus laevis. The open reading frame of psXEED was interrupted with a 40 bp insertion. Its untranslated region was identical with that of the functional XEED. Furthermore, the EED pseudogene was organized as a processed pseudogene in the genomic level. Interestingly, the EED pseudogene was transcribed both maternally and zygotically throughout embryogenesis.
