The Epidemiological, Clinical Features and Outcomes of Imported Chinese COVID-19 Patients Following Inactivated Vaccines Injection.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination had been demonstrated as an effective way to reduce the risk of coronavirus disease 2019 (COVID-19), and only a few vaccines suffered from SARS-CoV-2 infection. However, limited data concerning the clinical features of these vaccines infected with SARS-CoV-2 can be identified. Methods: We retrospectively collected and analyzed epidemiological and clinical characteristics data of the imported COVID-19 cases who received Chinese inactivated vaccines abroad. Data were extracted from electronic medical records from a designated hospital in the Shaanxi Province of China between March 22 and May 17, 2021. Results: Totally, 46 confirmed SARS-CoV-2 infection patients were enrolled. The mean age was 40.5 years (range 20-61), 41 (89.1%) are male. Eighteen (39.1%) patients were from Pakistan. Fourteen (30.4%) patients had at least one comorbidity. Forty (87.0%) and 6 cases were fully vaccinated and partly vaccinated. The time interval between vaccination and infection was 88 days (IQR, 33-123), 31 (67.4%) and 15 (32.6%) were asymptomatic and symptomatic cases, respectively. Fever (3/46, 6.5%) was the most common symptom; however, none had a body temperature higher than 38.0°C, and no severe case was observed. Notably, the rate of SARS-CoV-2 shedding discontinuation at 7 days after hospitalization in asymptomatic cases was higher than symptomatic one (93.5% vs 40%, P < 0.0001). Conclusion: Individuals who received Chinese inactivated vaccines abroad remain to have the probability of being infected with SARS-CoV-2, but all the vaccines infected with SARS-CoV-2 were asymptomatic or had mild symptoms with favorable clinical outcomes.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Depression, anxiety, and stress during the COVID-19 pandemic: comparison among higher education students in four countries in the Asia-Pacific region

The global COVID-19 pandemic is affecting the health of individuals and leading to psychological problems. Students in higher education who are graduating, facing online learning challenges, and future job opportunities are among the most at-risk group for psychological issues. Due to the new normal of the COVID-19 pandemic, limited studies have been conducted concerning the mental health of students, especially in the Asia-Pacific region. Therefore, this study aimed to assess student's depression, anxiety, and stress status in four countries in the Asia Pacific region, namely, Malaysia, Indonesia, Thailand, and China. This study employed a quantitative research design with a pool of 1,195 student participants. The DASS-21 questionnaire was used for data collection through an online platform to measure the severity of depression, anxiety, and stress. Descriptive statistics were conducted to achieve the research objectives, and all reliability values were reported greater than 0.70. Findings revealed that up to 38% of the students reported mild or moderate depression, anxiety, and stress, while 20.5% reported severe or extremely severe anxiety. Overall, anxiety was reported to be the most significant problem among the students, followed by depression and stress. Students are at risk of mental health challenges during the coronavirus pandemic, likely due to unexpected life changes. This study contributes an overview report of students' mental health problems and discusses the support and services in preventing students' psychological problems. The comprehensive discussion has provided scientific information and suggestion to policymakers in maintaining the student academic and welfare.