Dendritic cell targeting virus-like particle delivers mRNA for in vivo immunization (preprint)

mRNA vaccine was approved clinically in 2020. Future development includes delivering mRNA to dendritic cells (DCs) specifically to improve effectiveness and avoid off-target cytotoxicity. Here, we developed virus-like particles (VLPs) as a DC tropic mRNA vaccine vector and showed the prophylactic effects in both SARS-CoV-2 and HSV-1 infection models. The VLP mRNA vaccine elicited strong cytotoxic T cell immunity and durable antibody response with the spike-specific antibodies that lasted for more than 9 months. Importantly, we were able to target mRNA to DCs by pseudotyping VLP with engineered Sindbis virus glycoprotein and found the DC-targeting mRNA vaccine significantly enhanced the titer of antigen-specific IgG, protecting the hACE-2 mice from SARS-CoV-2 infection. Additionally, we showed DC-targeted mRNA vaccine also protected mice from HSV-1 infection when co-delivering the gB and gD mRNA. Thus, the VLP may serve as an in situ DC vaccine and accelerate the further development of mRNA vaccines.

A Self-Attention Mechanism Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images (preprint)

The new type of coronavirus is called COVID-19. The virus can cause respiratory diseases, accompanied by cough, fever, difficulty breathing, and in severe cases, it can also cause symptoms such as pneumonia. It began to spread at the end of 2019 and has now spread to all parts of the world. The limited test kits and increasing number of cases encourage us to propose a deep learning model that can help radiologists and clinicians use chest X-rays to detect COVID-19 cases and show the diagnostic features of pneumonia. In this study, our methods are: 1) Propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the network. 2) Using the deep convolutional neural network model DPN-SE, an attention mechanism is added on the basis of the DPN network, which greatly improves the performance of the network. 3) Use the lime interpretable library to mark the X-ray, the characteristic area on the medical image that is helpful for the doctor to make a diagnosis. The model we proposed can obtain better results with the least amount of data preprocessing given limited data. In general, the proposed method and model can effectively become a very useful tool for clinical practitioners and radiologists.

DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images (preprint)

Background and Objective: The new type of coronavirus is also called COVID-19. It began to spread at the end of 2019 and has now spread across the world. Until October 2020, It has infected around 37 million people and claimed about 1 million lives. We propose a deep learning model that can help radiologists and clinicians use chest X-rays to diagnose COVID-19 cases and show the diagnostic features of pneumonia. Methods: The approach in this study is: 1) we propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the model. 2) Our deep convolution neural network model DPN-SE adds a self-attention mechanism to the DPN network. The addition of a self-attention mechanism has greatly improved the performance of the network. 3) Use the Lime interpretable library to mark the feature regions on the X-ray medical image that helps doctors more quickly diagnose COVID-19 in people. Results: Under the same network model, the data with and without data enhancement is put into the model for training respectively. At last, comparing two experimental results: among the 10 network models with different structures, 7 network models have improved their effects after using data enhancement, with an average improvement of 1% in recognition accuracy. We propose that the accuracy and recall rates of the DPN-SE network are 93% and 98% of cases (COVID vs. pneumonia bacteria vs. viral pneumonia vs. normal). Compared with the original DPN, the respective accuracy is improved by 2%. Conclusion: The data augmentation method we used has achieved effective results on a small amount of data set, showing that a reasonable data augmentation method can improve the recognition accuracy without changing the sample size and model structure. Overall, the proposed method and model can effectively become a very useful tool for clinical radiologists.

Discovery of Pre-Existing Drugs that Suppress the Replication of SARS-CoV-2 in Vitro (preprint)

SARS-CoV-2, the causative agent of coronavirus disease 19 (COVID 19), is responsible for the ongoing pandemic but still lacks approved antivirals. Repurposing pre-existing FDA approved drugs presents a rapid approach for new therapeutic options. In the present study, we report that three pre-existing FDA-approved drugs, i.e., vapreotide, grazoprevir, and simeprevir, inhibit the replication of SARS-CoV-2 in cells. The E50 values of vapreotide, grazoprevir, and simeprevir against SARS-CoV-2 in Vero E6 cells was 3.98 ± 0.35 μM, 2.08 ± 0.13 μM, and 1.41 ± 0.12 μM, respectively. In vitro biochemical experiments further revealed that vapreotide, grazoprevir, and simeprevir efficiently inhibits the unwinding activity of the Nsp13 helicase of SARS-CoV-2 with IC50 values of ⁓10, ⁓2.5, and ⁓1.25 µM, respectively, providing signs for understanding their antiviral mechanism of action. Given their good safety profiles in their original indications, our study offices new insights in repurposing these drugs alone or in combination with other antivirals in the global fighting against SARS-CoV-2.Funding: This work was financially supported by the Youth Innovation Promotion Association CAS (to H.Y.), and the National Natural Science Foundation of China (No. 31770192 and No. 32070187 to H.Y.).Conflict of Interest: The authors declare no competing interests.

Clinical features of COVID-19 patients with comorbid coronary heart disease (preprint)

Background: In addition to the lungs, the coronavirus disease 2019 (COVID-19) also affects multiple organs throughout the body. The relationship between COVID-19 infection and cardiovascular disease, and the mechanisms by which this disease causes damage to the cardiovascular system are unclear. Coronary heart disease (CHD) is one of the common comorbidities of COVID-19, but there is insufficient evidence for its clinical features and impact on clinical outcomes. The aim of this study was to analyze the clinical characteristics of COVID-19 patients with comorbid CHD and the possible risk factors for the occurrence of critical illness. Methods: A single-center, retrospective study was conducted to analyze COVID-19 patients admitted to the Sino-French New City Campus of Tongji Hospital in Wuhan, Hubei Province and treated by the Peking University National Medical Assistance Team between January 29 and March 10, 2020. Patients testing positive for SARS-CoV-2 viral nucleic acid in nasopharyngeal swab specimens and who had comorbid CHD, were included in the study. Clinical data and laboratory test results of eligible patients were collected, and the factors associated with the occurrence of critical illness among these patients were evaluated. Results: A total of 205 patients were enrolled in this study, including 20 CHD patients and 185 non-CHD patients. The mean age was 66.7 years. Compared to non-CHD patients, more CHD patients had comorbid hypertension and diabetes (P < 0.05). In terms of laboratory tests, the CHD group did not differ significantly from the non-CHD group in blood routine, blood chemistry, and various inflammatory cytokines. More CHD patients experienced myocardial injury (25% vs 8.1% P < 0.031) and CHD patients were more likely to progress to critical illness (40% vs 16.8%P = 0.012). Univariate logistic regression analysis indicated that a history of CHD, occurrence of myocardial injury, high white blood cell (WBC) count, low lymphocyte count, and elevated levels of Cr, ferritin, IL-2R, IL-8 at admission were factors associated with the occurrence of critical illness. Multivariate regression analysis found that a history of CHD（OR=3.529, 95% CI =1.032-12.075, P =0.044），high WBC count（OR=1.289, 95% CI =1.136-1.463, P＜0.001） and low lymphocyte count（OR=0.215, 95% CI =0.075-0.616, P =0.004）were independent factors for the occurrence of critical illness among COVID-19 patients. Conclusion: COVID-19 patients with comorbid CHD commonly exhibited myocardial injury and were prone to developing critical illness. Among COVID-19 patients, a history of CHD，high WBC count and low lymphocyte count were independent risk factors for the occurrence of critical illness. Greater attention and vigilance are needed in this regard during clinical practice.

Comparison of Associations Between Glucocorticoids Treatment and Mortality in COVID-19 Patients and SARS Patients: A Systematic Review and Meta-Analysis (preprint)

BackgroundThe response to glucocorticoids treatment may be different between Covid-19 and SARS. MethodsIn this systematic review and meta-analysis, we searched studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, ClinicalTrials.gov, ICTRP from 2002 to October 7, 2020. We used fixed-effects and random-effects models to compute the risk ratio of death in the group receiving glucocorticoids treatment and the control group for COVID-19 and SARS, respectively.ResultsTen trials and 71 observational studies, with a total of 45935 patients, were identified. Glucocorticoids treatment, was associated with decreased all-cause mortality both in COVID-19 (risk ratio, 0.88; 95% confidence interval, 0.82 to 0.94; I2=26%) and SARS (0.48; 0.29 to 0.79; 10%), based on high quality evidence, as well as decreased all-cause mortality-including composite outcome of COVID-19 (0.89; 0.82 to 0.98; 0%). In subgroup analyses, all-cause mortality was significantly lower among COVID-19 patients being accompanied by severe ARDS but not mild ARDS, taking low-dose or pulse glucocorticoids, being critically severe but not only severe, being of critical severity and old but not young, being of critical severity and men but not women, non-early taking glucocorticoids and taking dexamethasone or methylprednisolone; but for SARS, lower mortality were observed among those who were taking medium-high dose glucocorticoids, being severe or critically severe, early taking glucocorticoids, and taking dexamethasone or prednisolone. ConclusionsGlucocorticoids treatment reduced mortality in COVID-19 and SARS patients of critical severity; however, different curative effects existed between the two diseases among subpopulations, mainly regarding sex- and age-specific effects, optimal doses and use timing of glucocorticoids.

Transmission Potential of Asymptomatic and Paucisymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections: A 3-Family Cluster Study in China.

Data concerning the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in asymptomatic and paucisymptomatic patients are lacking. We report a 3-family cluster of infections involving asymptomatic and paucisymptomatic transmission. Eight of 15 (53%) members from 3 families were confirmed with SARS-CoV-2 infection. Of 8 patients, 3 were asymptomatic and 1 was paucisymptomatic. An asymptomatic mother transmitted the virus to her son, and a paucisymptomatic father transmitted the virus to his 3-month-old daughter. SARS-CoV-2 was detected in the environment of 1 household. The complete genomes of SARS-CoV-2 from the patients were > 99.9% identical and were clustered with other SARS-CoV-2 sequences reported from China and other countries.