Protocol for characterizing the inhibition of SARS-CoV-2 infection by a protein of interest in cultured cells

Here, we present a protocol to characterize the antiviral ability of a protein of interest to SARS-CoV-2 infection in cultured cells, using MUC1 as an example. We use SARS-CoV-2 ΔN trVLP system, which utilizes transcription and replication-competent SARS-CoV-2 virus-like particles lacking nucleocapsid gene. We describe the optimized procedure to analyze protein interference of viral attachment and entry into cells, and RT-qPCR-based quantification of viral infection. The protocol can be applied to characterize more antiviral candidates and clarify their functioning stage. Graphical Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The Roles of Dipeptidyl Peptidase 4 (DPP4) and DPP4 Inhibitors in Different Lung Diseases: New Evidence.

CD26/Dipeptidyl peptidase 4 (DPP4) is a type II transmembrane glycoprotein that is widely expressed in various organs and cells. It can also exist in body fluids in a soluble form. DPP4 participates in various physiological and pathological processes by regulating energy metabolism, inflammation, and immune function. DPP4 inhibitors have been approved by the Food and Drug Administration (FDA) for the treatment of type 2 diabetes mellitus. More evidence has shown the role of DPP4 in the pathogenesis of lung diseases, since it is highly expressed in the lung parenchyma and the surface of the epithelium, vascular endothelium, and fibroblasts of human bronchi. It is a potential biomarker and therapeutic target for various lung diseases. During the coronavirus disease-19 (COVID-19) global pandemic, DPP4 was found to be an important marker that may play a significant role in disease progression. Some clinical trials on DPP4 inhibitors in COVID-19 are ongoing. DPP4 also affects other infectious respiratory diseases such as Middle East respiratory syndrome and non-infectious lung diseases such as pulmonary fibrosis, lung cancer, chronic obstructive pulmonary disease (COPD), and asthma. This review aims to summarize the roles of DPP4 and its inhibitors in infectious lung diseases and non-infectious diseases to provide new insights for clinical physicians.

Artificial Intelligence for COVID-19: A Systematic Review.

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis.

BACKGROUND: There is limited information on the difference in epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic. METHODS: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes. RESULTS: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64). CONCLUSIONS: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

Clinical characteristics and risk factors for symptomatic venous thromboembolism in hospitalized COVID-19 patients: A multicenter retrospective study.

BACKGROUND: High incidence of asymptomatic venous thromboembolism (VTE) has been observed in severe COVID-19 patients, but the characteristics of symptomatic VTE in general COVID-19 patients have not been described. OBJECTIVES: To comprehensively explore the prevalence and reliable risk prediction for VTE in COVID-19 patients. METHODS/RESULTS: This retrospective study enrolled all COVID-19 patients with a subsequent VTE in 16 centers in China from January 1 to March 31, 2020. A total of 2779 patients were confirmed with COVID-19. In comparison to 23,434 non-COVID-19 medical inpatients, the odds ratios (ORs) for developing symptomatic VTE in severe and non-severe hospitalized COVID-19 patients were 5.94 (95% confidence interval [CI] 3.91-10.09) and 2.79 (95% CI 1.43-5.60), respectively. When 104 VTE cases and 208 non-VTE cases were compared, pulmonary embolism cases had a higher rate for in-hospital death (OR 6.74, 95% CI 2.18-20.81). VTE developed at a median of 21 days (interquartile range 13.25-31) since onset. Independent factors for VTE were advancing age, cancer, longer interval from symptom onset to admission, lower fibrinogen and higher D-dimer on admission, and D-dimer increment (DI) ≥1.5-fold; of these, DI ≥1.5-fold had the most significant association (OR 14.18, 95% CI 6.25-32.18, p = 2.23 × 10-10 ). A novel model consisting of three simple coagulation variables (fibrinogen and D-dimer levels on admission, and DI ≥1.5-fold) showed good prediction for symptomatic VTE (area under the curve 0.865, 95% CI 0.822-0.907, sensitivity 0.930, specificity 0.710). CONCLUSIONS: There is an excess risk of VTE in hospitalized COVID-19 patients. This novel model can aid early identification of patients who are at high risk for VTE.