Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19.

Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.

Genomic variation, origin tracing, and vaccine development of SARS-CoV-2: A systematic review.

COVID-19 has spread globally to over 200 countries with more than 40 million confirmed cases and one million deaths as of November 1, 2020. The SARS-CoV-2 virus, leading to COVID-19, shows extremely high rates of infectivity and replication, and can result in pneumonia, acute respiratory distress, or even mortality. SARS-CoV-2 has been found to continue to rapidly evolve, with several genomic variants emerging in different regions throughout the world. In addition, despite intensive study of the spike protein, its origin, and molecular mechanisms in mediating host invasion are still only partially resolved. Finally, the repertoire of drugs for COVID-19 treatment is still limited, with several candidates still under clinical trial and no effective therapeutic yet reported. Although vaccines based on either DNA/mRNA or protein have been deployed, their efficacy against emerging variants requires ongoing study, with multivalent vaccines supplanting the first-generation vaccines due to their low efficacy against new strains. Here, we provide a systematic review of studies on the epidemiology, immunological pathogenesis, molecular mechanisms, and structural biology, as well as approaches for drug or vaccine development for SARS-CoV-2.

A Systematic Analysis on COVID-19 Patients in Inner Mongolia Based on Dynamic Monitoring.

COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish COVID-19 from common pneumonia. In this study, we reported multiple clinical feature analyses on COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed COVID-19 patients, 219 pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting COVID-19. As a result, among the tested clinical characteristics, WBC, hemoglobin, C-reactive protein (CRP), ALT, and Cr were significantly different between COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting COVID-19.

Association of the insulin resistance marker TyG index with the severity and mortality of COVID-19.

BACKGROUND: The triglyceride and glucose index (TyG) has been proposed as a marker of insulin resistance. This study aims to evaluate the association of the TyG index with the severity and mortality of coronavirus disease 2019 (COVID-19). METHODS: The study included a cohort of 151 patients with COVID-19 admitted in a tertiary teaching hospital in Wuhan. Regression models were used to investigate the association between TyG with severity and mortality of COVID-19. RESULTS: In this cohort, 39 (25.8%) patients had diabetes, 62 (41.1%) patients were severe cases, while 33 (22.0%) patients died in hospital. The TyG index levels were significantly higher in the severe cases and death group (mild vs. severe 8.7 ± 0.6 vs. 9.2 ± 0.6, P < 0.001; survivor vs. deceased 8.8 ± 0.6 vs. 9.3 ± 0.7, P < 0.001), respectively. The TyG index was significantly associated with an increased risk of severe case and mortality, after controlling for potential confounders (OR for severe case, 2.9, 95% CI 1.2-6.3, P = 0.007; OR for mortality, 2.9, 95% CI 1.2-6.7, P = 0.016). The associations were not statistically significant for further adjustment of inflammatory factors. CONCLUSION: TyG index was closely associated with the severity and morbidity in COVID-19 patients, thus it may be a valuable marker for identifying poor outcome of COVID-19.

Clinical characteristics and outcomes of patients with severe covid-19 with diabetes.

OBJECTIVE: This study explores the clinical characteristics of patients with diabetes with severe covid-19, and the association of diabetes with survival duration in patients with severe covid-19. RESEARCH DESIGN AND METHODS: In this single-center, retrospective, observational study, the clinical and laboratory characteristics of 193 patients with severe covid-19 were collected. 48 patients with severe covid-19 had diabetes, and 145 patients (ie, the controls) did not have diabetes. A severe case was defined as including at least one of the following criteria: (1) Respiratory rate >30/min. (2) Oxygen saturation ≤93%. (3) PaO2/FiO2≤300 mm Hg. (4) Patients, either with shock or respiratory failure, requiring mechanical ventilation, or combined with other organ failure, requiring admission to intensive care unit (ICU). RESULTS: Of 193 patients with severe covid-19, 48 (24.9%) had diabetes. Compared with patients with severe covid-19 without diabetes, patients with diabetes were older, susceptible to receiving mechanical ventilation and admission to ICU, and had higher mortality. In addition, patients with severe covid-19 with diabetes had higher levels of leukocyte count, neutrophil count, high-sensitivity C reaction protein, procalcitonin, ferritin, interleukin (IL) 2 receptor, IL-6, IL-8, tumor necrosis factor α, D-dimer, fibrinogen, lactic dehydrogenase and N-terminal pro-brain natriuretic peptide. Among patients with severe covid-19 with diabetes, more non-survivors were men (30 (76.9%) vs 9 (23.1%)). Non-survivors had severe inflammatory response, and cardiac, hepatic, renal and coagulation impairment. Finally, the Kaplan-Meier survival curve showed a trend towards poorer survival in patients with severe covid-19 with diabetes than patients without diabetes. The HR was 1.53 (95% CI 1.02 to 2.30; p=0.041) after adjustment for age, sex, hypertension, cardiovascular disease and cerebrovascular disease by Cox regression. The median survival durations from hospital admission in patients with severe covid-19 with and without diabetes were 10 days and 18 days, respectively. CONCLUSION: The mortality rate in patients with severe covid-19 with diabetes is considerable. Diabetes may lead to an increase in the risk of death.

CLINICAL CHARACTERISTICS OF 28 PATIENTS WITH DIABETES AND COVID-19 IN WUHAN, CHINA.

Objective: Previous studies on coronavirus disease 2019 (COVID-19) were based on information from the general population. We aimed to further clarify the clinical characteristics of diabetes with COVID-19. Methods: Twenty-eight patients with diabetes and COVID-19 were enrolled from January 29, 2020, to February 10, 2020, with a final follow-up on February 22, 2020. Epidemiologic, demographic, clinical, laboratory, treatment, and outcome data were analyzed. Results: The average age of the 28 patients was 68.6 ± 9.0 years. Most (75%) patients were male. Only 39.3% of the patients had a clear exposure of COVID-19. Fever (92.9%), dry cough (82.1%), and fatigue (64.3%) were the most common symptoms, followed by dyspnea (57.1%), anorexia (57.1%), diarrhea (42.9%), expectoration (25.0%), and nausea (21.4%). Fourteen patients were admitted to the intensive care unit (ICU). The hemoglobin A1c level was similar between ICU and non-ICU patients. ICU patients had a higher respiratory rate, higher levels of random blood glucose, aspartate transaminase, bilirubin, creatine, N-terminal prohormone of brain natriuretic peptide, troponin I, D-dimers, procalcitonin, C-reactive protein, ferritin, interleukin (IL)-2R, IL-6, and IL-8 than non-ICU patients. Eleven of 14 ICU patients received noninvasive ventilation and 7 patients received invasive mechanical ventilation. Twelve patients died in the ICU group and no patients died in the non-ICU group. Conclusion: ICU cases showed higher rates of organ failure and mortality than non-ICU cases. The poor outcomes of patients with diabetes and COVID-19 indicated that more supervision is required in these patients. Abbreviations: COVID-19 = coronavirus disease 2019; ICU = intensive care unit; MERS-CoV = middle East respiratory syndrome-related coronavirus; 2019- nCoV = 2019 novel coronavirus; NT-proBNP = N-terminal prohormone of brain natriuretic peptide; SARS-CoV = severe acute respiratory syndrome-related coronavirus.