Risk assessment of global COVID-19 pandemic in October 2022

Objective: To assess the global epidemic of Coronavirus disease 2019(COVID-19) in October 2022 and the risk of importation.

Variation characteristics and variants identification technique of SARS-CoV-2

The outbreak of COVID-19 caused by SARS-CoV-2 in 2019 has become a major global health crisis. There are multiple variants of SARS-CoV-2 identified, with significant differences in the viral transmission capacity and toxicity.SARS-CoV-2 variation has become one of the major challenges in the prevention and control of the pandemic.The identification of SARS-CoV-2 variants and understanding the variation characteristics may significantly help to curb the spread of SARS-CoV-2, adopt comprehensive and effective disease control measures and choose reasonable treatment plans.In this paper, the variation features of SARS-CoV-2 and the mutation testing techniques were reviewed.

A Network Pharmacology Study to Uncover the Multiple Molecular Mechanism of the Chinese Patent Medicine Toujiequwen Granules in the Treatment of Corona Virus Disease 2019 (COVID-19).

Since the outbreak of the novel corona virus disease 2019 (COVID-19) at the end of 2019, specific antiviral drugs have been lacking. A Chinese patent medicine Toujiequwen granules has been promoted in the treatment of COVID-19. The present study was designed to reveal the molecular mechanism of Toujiequwen granules against COVID-19. A network pharmacological method was applied to screen the main active ingredients of Toujiequwen granules. Network analysis of 149 active ingredients and 330 drug targets showed the most active ingredient interacting with many drug targets is quercetin. Drug targets most affected by the active ingredients were PTGS2, PTGS1, and DPP4. Drug target disease enrichment analysis showed drug targets were significantly enriched in cardiovascular diseases and digestive tract diseases. An "active ingredient-target-disease" network showed that 57 active ingredients from Toujiequwen granules interacted with 15 key targets of COVID-19. There were 53 ingredients that could act on DPP4, suggesting that DPP4 may become a potential new key target for the treatment of COVID-19. GO analysis results showed that key targets were mainly enriched in the cellular response to lipopolysaccharide, cytokine activity and other functions. KEGG analysis showed they were mainly concentrated in viral protein interaction with cytokine and cytokine receptors and endocrine resistance pathway. The evidence suggests that Toujiequwen granules might play an effective role by improving the symptoms of underlying diseases in patients with COVID-19 and multi-target interventions against multiple signaling pathways related to the pathogenesis of COVID-19.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study.

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Machine Learning-based CT Radiomics Model Distinguishes COVID-19 From Non-COVID-19 Pneumonia (preprint)

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the region of interest (ROI), and the radiomic features were extracted. The Support Vector Machine(SVM) model was built on the combination of the 4 groups of features, including radiomic features, traditional radiological features, quantifying features and clinical features, by repeated cross-validation procedure and the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: For the SVM model that built on the combination of 4 groups of features(integrated model), the per-exam AUC of 0.925(95% CI: 0.856 to 0.994) was reached for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816(95% CI: 0.651 to 0.917) and 0.923(95% CI: 0.621 to 0.996), respectively. For the SVM models that built on radiomic features, radiological features, quantifying features and clinical features individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607 and 0.739 respectively, significantly lower than the integrated model, except for the radiomic model.Conclusion: The machine learning-based CT radiomics models may accurately detect COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

CT-based Rapid Triage of COVID-19 Patients: Risk Prediction and Progression Estimation of ICU Admission, Mechanical Ventilation, and Death of Hospitalized Patients (preprint)

The wave of COVID-19 continues to overwhelm the medical resources, especially the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). Here we performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from 9 external hospitals, achieved satisfying performance for predicting ICU, MV and death of COVID-19 patients (AUROC 0.916, 0.919 and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943 and 0.856). Both clinical and image features showed complementary roles in events prediction and provided accurate estimates to the time of progression (p

Gastrointestinal symptoms and fecal nucleic acid testing of children with 2019 coronavirus disease: a systematic review and meta-analysis.

In order to understand the clinical manifestations and incidence of gastrointestinal symptoms of coronavirus disease (COVID-19) in children and discuss the importance of fecal nucleic acid testing.We retrospectively analyzed studies on gastrointestinal symptoms and fecal nucleic acid detection in pediatric COVID-19 patients from January 1, 2020 to August 10, 2020, including prospective clinical studies and case reports. The results of fecal nucleic acid detection were analyzed systematically. Stata12.0 software was used for meta-analysis.The results showed that the most common gastrointestinal symptoms in children with COVID-19 were vomiting and diarrhea, with a total incidence of 17.7% (95% Cl 13.9-21.5%). However, the prevalence of gastrointestinal symptoms in other countries (21.1%, 95% CI 16.5-25.7%) was higher compared to China (12.9%, 95% CI 8-17.7%). In Wuhan, the pooled prevalence was much higher (41.3%, 95% CI 3.2-79.4%) compared to areas outside Wuhan in China (7.1%, 95% CI 4.0-10.3%). The positive rate of fecal nucleic acid testing in COVID-19 children was relatively high at 85.8% (91/106). Additionally, 71.2% (52/73) were still positive for fecal nucleic acid after respiratory tract specimens turned negative. One and two weeks after the respiratory tract specimens turned nucleic acid-negative, 45.2% (33/73) and 34.2% (25/73) patients, respectively, remained fecal nucleic acid-positive. The longest interval between the respiratory tract specimens turning negative and fecal specimens turning negative exceeded 70 days. Conclusions and relevance: gastrointestinal symptoms in pediatric COVID-19 are relatively common. Attention should be paid to the detection of fecal nucleic acids in children. Fecal nucleic acid-negative status should be considered as one of the desegregation standards.

Gastrointestinal symptoms and fecal nucleic acid testing of children with 2019 coronavirus disease: a systematic review and meta-analysis (preprint)

In order to understand the clinical manifestations and incidence of gastrointestinal symptoms of coronavirus disease (COVID-19) in children and discuss the importance of fecal nucleic acid testing.We retrospectively analyzed studies on gastrointestinal symptoms and fecal nucleic acid detection in pediatric COVID-19 patients from January 1, 2020 to August 10, 2020, including prospective clinical studies and case reports. The results of fecal nucleic acid detection were analyzed systematically. Stata12.0 software was used for meta-analysis.The results showed that the most common gastrointestinal symptoms in children with COVID-19 were vomiting and diarrhea, with a total incidence of 17.7% (95% Cl: 13.9%-21.5%). However, the prevalence of gastrointestinal symptoms in other countries (21.1%, 95% CI: 16.5%-25.7%) was higher compared to China (12.9%, 95% CI: 8%-17.7%). In Wuhan, the pooled prevalence was much higher (41.3%, 95 % CI: 3.2%-79.4%) compared to areas outside Wuhan in China (7.1%, 95 % CI: 4.0%-10.3%).The positive rate of fecal nucleic acid testing in COVID-19 children was relatively high at 85.8% (91/106). Additionally, 71.2% (52/73) were still positive for fecal nucleic acid after respiratory tract specimens turned negative. One and two weeks after the respiratory tract specimens turned nucleic acid-negative, 45.2% (33/73) and 34.2% (25/73) patients, respectively, remained fecal nucleic acid-positive. The longest interval between the respiratory tract specimens turning negative and fecal specimens turning negative exceeded 70 days.Conclusions and Relevance:Gastrointestinal symptoms in pediatric COVID-19 are relatively common. Attention should be paid to the detection of fecal nucleic acids in children. Fecal nucleic acid-negative status should be considered as one of the desegregation standards.

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia (preprint)

Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.