Chimeric mRNA-based COVID-19 vaccine induces protective immunity against Omicron and Delta variants.

The emerging SARS-CoV-2 variants of concern (VOCs) exhibit enhanced transmission and immune escape, reducing the effectiveness of currently approved mRNA vaccines. To achieve wider coverage of VOCs, we first constructed a cohort of mRNAs harboring a furin cleavage mutation in the spike (S) protein of predominant VOCs, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). The mutation abolished the cleavage between the S1 and S2 subunits. Systematic evaluation in vaccinated mice discovered that individual VOC mRNAs elicited strong neutralizing activity in a VOC-specific manner. In particular, the neutralizing antibodies (nAb) produced by immunization with Beta-Furin and Washington (WA)-Furin mRNAs showed potent cross-reactivity with other VOCs. However, neither mRNA elicited strong neutralizing activity against the Omicron variant. Hence, we further developed an Omicron-specific mRNA vaccine that restored protection against the original Omicron variant and some sublineages. Finally, to broaden the protection spectrum of the new Omicron mRNA vaccine, we engineered an mRNA-based chimeric immunogen by introducing the receptor-binding domain of Delta variant into the entire S antigen of Omicron. The resultant chimeric mRNA induced potent and broadly nAbs against Omicron and Delta, which paves the way to developing new vaccine candidates to target emerging variants in the future.

Demographic, Virological Characteristics and Prognosis of Asymptomatic COVID-19 Patients in South China.

BACKGROUND: Asymptomatic transmission is a major concern for SARS-CoV-2 community spread; however, little information is available on demographic, virological characteristics and prognosis of asymptomatic cases. METHODS: All COVID-19 patients hospitalized in Guangdong Province from September 1, 2020 to February 28, 2021, were included and were divided into asymptomatic and symptomaticgroup. The source country of all patients, clinical laboratory test results, the genotype of virus and the time of SARS-CoV-2 RNA turning negative or hospitalization were confirmed. RESULTS: Total 233 patients from 57 different countries or regions were included, with 83 (35.6%) asymptomatic and 150 (64.4%) symptomatic patients. Asymptomatic cases were younger (P = 0.019), lower rate in comorbidities (P = 0.021) such as hypertension (P = 0.083) and chronic liver disease (P = 0.045), lower PCT (P = 0.021), DDI (P < 0.001) and ALT (P = 0.029), but higher WBC count (P = 0.002) and lymphocyte (P = 0.011) than symptomatic patients. As for SARS-CoV-2 subtypes, patients infected with B.1.1 (53.8%), B.1.351 (81.8%) and B.1.524 (60%) are mainly asymptomatic, while infected with B, B.1, B.1.1.63, B.1.1.7, B.1.36, B.1.36.1, B.1.36.16, B.1.5 and B.6 were inclined to be symptomatic. Patients infected with variant B.1.351 and B.1.524 spent longer time in SARS-CoV-2 RNA turn negative (26 days, P = 0.085; 41 days, P = 0.007) and hospitalization (28 days, P = 0.085; 43 days, P = 0.004). CONCLUSIONS: The asymptomatic cases are prone to develop in patients with younger age, less comorbidities andinfected with B.1.1 and B.1.524 variants. More attention should be paid for lineage B.1.524 because it can significantly prolong the SARS-CoV-2 RNA negative conversion time and hospitalization in infected cases.

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

Prevalence of depression and its association with quality of life in clinically stable patients with COVID-19.

INTRODUCTION: High risk of mental health problems is associated with Coronavirus Disease 2019 (COVID-19). This study explored the prevalence of depressive symptoms (depression hereafter) and its relationship with quality of life (QOL) in clinically stable patients with COVID-19. METHODS: This was an online survey conducted in COVID-19 patients across five designated isolation hospitals for COVID-19 in Hubei province, China. Depression and QOL were assessed with standardized instruments. RESULTS: A total of 770 participants were included. The prevalence of depression was 43.1% (95%CI: 39.6%-46.6%). Binary logistic regression analysis found that having a family member infected with COVID-19 (OR=1.51, P = 0.01), suffering from severe COVID-19 infection (OR=1.67, P = 0.03), male gender (OR=0.53, P<0.01), and frequent social media use to obtain COVID-19 related information (OR=0.65, P<0.01) were independently associated with depression. Patients with depression had lower QOL than those without. CONCLUSION: Depression is highly prevalent in clinically stable patients with COVID-19. Regular screening and appropriate treatment of depression are urgently warranted for this population.