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Background: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs. [ABSTRACT FROM AUTHOR]

Background: Fatigue is a common symptom of long COVID syndrome. Compared to male survivors, females have a higher incidence of post-COVID fatigue. Therefore, long-term follow-up is necessary to understand which groups of females are more vulnerable to post-COVID fatigue. Methods: This is a nested case–control study of female COVID-19 survivors who were discharged from two designated hospitals in Wuhan, China in 2020, and received 2-year follow-up from March 1 to April 6, 2022. All patients completed the Checklist Individual Strength-subscale subjective fatigue (CIS-fatigue), a chronic obstructive pulmonary disease (COPD) assessment test (CAT), and the Hospital Anxiety and Depression Scale (HADS; including the HADS-Anxiety [HADS-A] and the HADS-Depression [HADS-D]). Individuals with CIS-fatigue scores of 27 or higher were classified as cases. The risk factors for fatigue was analysed with multivariable logistic regression analysis. Results: A total of 899 female COVID-19 survivors were enrolled for analysis, including 47 cases and 852 controls. Compared with controls, cases had higher CAT, HADS-A and HADS-D scores, and showed a higher prevalence of symptoms, including anxiety (cases vs. controls, 44.7% vs. 4.0%, p < 0.001), chest tightness (21.2% vs. 2.3%, p < 0.001), dyspnoea (19.1% vs. 0.8%, p < 0.001) and so on. In multivariable logistic regression analysis, age (OR, 1.03; 95% CI, 1.01–1.06; p = 0.02) and cerebrovascular disease (OR, 11.32; 95% CI, 2.87–43.00; p < 0.001) were risk factors for fatigue. Fatigue had a statistically significant moderate correlation with depression (r = 0.44, p < 0.001), but not with CAT ≥ 10. Conclusion: Female COVID-19 patients who had cerebrovascular disease and older age have higher risk of fatigue. Patients with fatigue have higher CAT scores, and are more likely to have concurrent depression. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has posed significant threats to both the physical and psychological health of healthcare workers working in the front-line combating COVID-19. However, studies regarding the medium to long term impact of COVID-19 on mental health among healthcare workers are limited. Therefore, we conducted this cross-sectional survey to investigate the prevalence, factors and impact of post-traumatic stress disorder (PTSD) in healthcare workers exposed to COVID-19 8 months after the end of the outbreak in Wuhan, China. Methods: A web-based questionnaire was delivered as a link via the communication application WeChat to those healthcare workers who worked at several COVID-19 units during the outbreak (from December 2019 to April 2020) in Wuhan, China. The questionnaire included questions on social-demographic data, the post-traumatic stress disorder checklist-5 (PCL-5), the family care index questionnaire (Adaptation, Partnership, Growth, Affection and Resolve, APGAR), and the quality-of-life scale (QOL). The prevalence, risk and protective factors, and impact of PTSD on healthcare workers were subsequently analyzed. Results: Among the 659 participants, 90 healthcare workers were still suffering from PTSD 8 months after the end of the outbreak of COVID-19 in Wuhan, in which avoidance and negative impact were the most affected dimensions. Suffering from chronic disease, experiencing social isolation, and job dissatisfaction came up as independent risk factors for PTSD, while obtaining COVID-19 related information at an appropriate frequency, good family function, and working in well-prepared mobile cabin hospitals served as protective factors. The impact of PTSD on COVID-19 exposed healthcare workers was apparent by shortened sleeping time, feeling of loneliness, poorer quality of life and intention to resign. Conclusions: Eight months after the end of the COVID-19 outbreak in Wuhan, the level of PTSD in healthcare workers exposed to COVID-19 was still high. Apart from the commonly recognized risk factors, comorbid chronic disease was identified as a new independent risk factor for developing PTSD. For countries where the pandemic is still ongoing or in case of future outbreaks of new communicable diseases, this study may contribute to preventing cases of PTSD in healthcare workers exposed to infectious diseases under such circumstances. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and became pandemic after emerging in Wuhan, China, in December 2019. Several studies have been conducted to understand the key features of COVID-19 and its public health impact. However, the prognostic factors of COVID-19 are not well studied in the African setting. In this study, we aim to determine the epidemiological and clinical features of COVID-19 cases, immunological and virological courses, interaction with nutritional status, and response to treatment for COVID-19 patients in Ethiopia.Methods: A multi-center cohort study design will be performed. Patients with confirmed COVID-19 infection admitted to selected treatment centers will be enrolled irrespective of their symptoms and followed-up for 12 months. Baseline epidemiological, clinical, laboratory and imaging data will be collected from treatment records, interviews, physical measurements, and biological samples. Follow-up data collection involves treatment and prognostic outcomes to be measured using different biomarkers and clinical parameters. Data collection will be done electronically using the Open Data Kit (ODK) software package and then exported to STATA/SPSS for analysis. Both descriptive and multivariable analyses will be performed to assess the independent determinants of the treatment outcome and prognosis to generate relevant information for informed prevention and case management. The primary outcomes of this study are death/survival and viral shedding. Secondary outcomes include epidemiological characteristics, clinical features, genetic frequency shifts (genotypic variations), and nutritional status.Discussion: This is the first large prospective cohort study of patients in hospitals with COVID-19 in Ethiopia. The results will enable us to better understand the epidemiology of SARS-CoV-2 in Africa. This study will also provide useful information for effective public health measures and future pandemic preparedness and in response to outbreaks. It will also support policymakers in managing the epidemic based on scientific evidence.Trial Registration: The Protocol prospectively registered in ClinicalTrials.gov (NCT04584424) on 30 October, 2020. [ABSTRACT FROM AUTHOR]