Depression among schoolchildren and adolescents aged 9-17 years during the outbreak of COVID­19 in Beijing: a cross-sectional online survey.

The COVID-19 outbreak and related confinement have highly impacted psychological health among children and adolescents. This study aimed to explore the potential risk factors for depression among primary and middle school students and provide advices for psychological interventions during the outbreaks. An online cross-sectional survey was conducted among 18 primary and middle school students via quota sampling in Beijing during March 2020. The Center for Epidemiological Studies Depression Scale (CES-D) was used to assess depression. Differences between characteristics and depression were examined by chi-square tests. Multivariate logistic regression was used to reveal the potential risk factors for depression. A total of 7377 participants were included. The proportion of depression was 29.7%. Students in rural areas, with higher school categories, in graduating grades, with poor or excessive sleep duration, and without daily exercise were associated with a higher proportion of depression. Furthermore, students with a higher knowledge performance of COVID-19 showed a lower proportion of depression (odds ratio [OR] = 0.900, 95% confidence intervals [95% CI]: 0.888-0.913). Students who worried about academic performance (OR = 1.919, 95% CI: 1.718-2.144) or COVID-19 infection (OR = 1.450, 95% CI: 1.268-1.658) exhibited a high proportion of depression. The proportion of depression among primary and middle school students was negatively associated with the knowledge score and positively associated with their worry. Our findings suggest that psychological intervention might be more necessary for students with specific characteristics.

Development of a panel of three multiplex allele-specific qRT-PCR assays for quick differentiation of recombinant variants and Omicron subvariants of SARS-CoV-2.

Quick differentiation of the circulating variants and the emerging recombinant variants of SARS-CoV-2 is essential to monitor their transmission. However, the widely used gene sequencing method is time-consuming and costly when facing the viral recombinant variants, because partial or whole genome sequencing is required. Allele-specific real time RT-PCR (qRT-PCR) represents a quick and cost-effective method in SNP genotyping and has been successfully applied for SARS-CoV-2 variant screening. In the present study, we developed a panel of 3 multiplex allele-specific qRT-PCR assays targeting 12 key differential mutations for quick differentiation of SARS-CoV-2 recombinant variants (XD and XE) and Omicron subvariants (BA.1 and BA.2). Two parallel multiplex qRT-PCR reactions were designed to separately target the protype allele and the mutated allele of the four mutations in each allele-specific qRT-PCR assay. The variation of Cp values (ΔCp) between the two multiplex qRT-PCR reactions was applied for mutation determination. The developed multiplex allele-specific qRT-PCR assays exhibited outstanding analytical sensitivities (with limits of detection [LoDs] of 2.97-27.43 copies per reaction), wide linear detection ranges (107-100 copies per reaction), good amplification efficiencies (82% to 95%), good reproducibility (Coefficient of Variations (CVs) < 5% in both intra-assay and inter-assay tests) and clinical performances (99.5%-100% consistency with Sanger sequencing). The developed multiplex allele-specific qRT-PCR assays in this study provide an alternative tool for quick differentiation of SARS-CoV-2 recombinant variants (XD and XE) and Omicron subvariants (BA.1 and BA.2).

Clinical and virological course of patients with coronavirus disease 2019 in Jiangsu province, China: a retrospective, multi-center cohort study.

BACKGROUND: The clinical and virological course of patients with coronavirus disease 2019 (COVID-19) are lacking. We aimed to describe the clinical and virological characteristics of COVID-19 patients from 10 designated hospitals in 10 cities of Jiangsu province, China. The factors associated with the clearance of SARS-CoV-2 were investigated. METHODS: A total of 328 hospitalized patients with COVID-19 were retrospectively recruited. The epidemiological, clinical, laboratory, radiology and treatment data were collected. The associated factors of SARS-CoV-2 clearance were analyzed. RESULTS: The median duration of hospitalization was 16.0 days (interquartile range [IQR] 13.0-21.0 days). On multivariate Cox regression analysis, age > 60 years (hazard ratio [HR] 0.643, 95% confidence interval [CI] 0.454-0.911, P = 0.013) was associated with the delayed SARS-CoV-2 clearance, while the atomized inhalation of interferon α-2b could improve the clearance of SARS-CoV-2 (HR, 1.357, 95% CI 1.050-1.755, P = 0.020). Twenty-six (7.9%) patients developed respiratory failure and 4 (1.2%) patients developed ARDS. Twenty (6.1%) patients were admitted to the ICU, while no patient was deceased. CONCLUSIONS: Our study found that age > 60 years was associated with the delayed SARS-CoV-2 clearance, while treated with atomized inhalation of interferon α-2b could promote the clearance of SARS-CoV-2.

Caring for anxiety among adults in the face of COVID-19: A cross-sectional online survey.

BACKGROUND: The outbreak of COVID-19 poses a challenge to psychological resilience. The aim of this study was to evaluate the prevalence of anxiety and identify risk and protective factors associated with the presence of anxiety symptoms in the face of COVID-19 among adults. METHODS: A cross-sectional online survey was conducted in adults from March 2nd to March 16th 2020. The self-rating anxiety scale (SAS) was used to measure the status of anxiety. Unconditional multivariate logistic regression was performed to identify the factors associated with anxiety. RESULTS: Among the 7144 respondents, 9.3% met the criteria for anxiety risk based on the SAS. Symptoms of anxiety were more prevalent among farmer (OR=1.43, 95%CI: 1.03-1.99), respondents lived in urban out of Beijing during the COVID-19 outbreak (OR=1.73, 95%CI: 1.14-2.63), and slept less than six hours per day (OR=2.64, 95%CI: 1.96-3.57). Compared to participants who didn't exercise, a lower risk of anxiety was observed in those exercised 30-60 minutes/day (OR=0.62, 95%CI: 0.41-0.94) and more than 60 minutes/day (OR=0.57, 95%CI: 0.37-0.88). And compared with participants whose knowledge and perceptions of COVID-9 scores in lower quartile, the OR (95%CI) for the second, third and upper quartile were 0.58 (0.46, 0.73), 0.48 (0.37, 0.61) and 0.42(0.33, 0.52), respectively. LIMITATIONS: No diagnostic interview for mental disorders was administered in the original studies limiting analysis of sensitivity and specificity of the Swahili PHQ-9. CONCLUSION: There was a high level of anxiety in the face of COVID-19 among adults. The results point to characteristics of adults in particular need for attention to anxiety and suggest possible targets for intervention such as strengthening of physical activity and knowledge and perceptions of COVID-19.

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.

Clinical findings of patients with coronavirus disease 2019 in Jiangsu province, China: A retrospective, multi-center study.

Limited data are available for clinical characteristics of patients with coronavirus disease 2019 (COVID-19) outside Wuhan. This study aimed to describe the clinical characteristics of COVID-19 and identify the risk factors for severe illness of COVID-19 in Jiangsu province, China. Clinical data of hospitalized COVID-19 patients were retrospectively collected in 8 hospitals from 8 cities of Jiangsu province, China. Clinical findings of COVID-19 patients were described and risk factors for severe illness of COVID-19 were analyzed. By Feb 10, 2020, 202 hospitalized patients with COVID-19 were enrolled. The median age of patients was 44.0 years (interquartile range, 33.0-54.0). 55 (27.2%) patients had comorbidities. At the onset of illness, the common symptoms were fever (156 [77.2%]) and cough (120 [59.4%]). 66 (32.7%) patients had lymphopenia. 193 (95.5%) patients had abnormal radiological findings. 11 (5.4%) patients were admitted to the intensive care unit and none of the patients died. 23 (11.4%) patients had severe illness. Severe illness of COVID-19 was independently associated with body mass index (BMI) ≥ 28 kg/m2 (odds ratio [OR], 9.219; 95% confidence interval [CI], 2.731 to 31.126; P<0.001) and a known history of type 2 diabetes (OR, 4.326; 95% CI, 1.059 to 17.668; P = 0.041). In this case series in Jiangsu Province, COVID-19 patients had less severe symptoms and had better outcomes than the initial COVID-19 patients in Wuhan. The BMI ≥ 28 kg/m2 and a known history of type 2 diabetes were independent risk factors of severe illness in patients with COVID-19.