Safety and immunogenicity of inactivated SARS-CoV-2 vaccines in healthy individuals: protocol for a systematic review and meta-analysis.

INTRODUCTION: COVID-19 has spread with high morbidity and mortality worldwide. Many inactivated SARS-CoV-2 vaccines are being tested at various clinical trial stages for the control and prevention of COVID-19. We aim to comprehensively and objectively evaluate the safety and immunogenicity of inactivated SARS-CoV-2 vaccines in healthy individuals through a systematic review and meta-analysis of randomised controlled trials (RCTs). METHODS AND ANALYSIS: We will search electronic databases of PubMed, the Cochrane Library, Web of Science and EMBASE for RCTs from inception to 31 December 2021. We will also search conference abstracts, reference lists, and grey literature of all available records. Two reviewers will independently screen and extract information from the literature. Bias and the quality of included studies will be evaluated with the risk-bias assessment tool provided by the Cochrane Collaboration. Statistical analysis will be performed using Cochrane's Review Manager (RevMan), V.5.3. ETHICS AND DISSEMINATION: Ethics approval and patient informed consent are not required because we will be including published literature only. The findings of this research will be disseminated in a peer-reviewed journal and likely through other scientific events such as conferences, seminars and symposia. PROSPERO REGISTRATION NUMBER: CRD42021266285.

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.

The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists' subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n = 75), progressive (n = 58), severe (n = 75), and absorption (n = 76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.