A bioengineered pseudovirus nanoparticle displaying SARS-CoV 2 RBD fully protects mice from mortality and weight loss caused by SARS-CoV 2 challenge.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused considerable morbidity and mortality worldwide. Although authorized COVID-19 vaccines have been shown highly effective, their significantly lower efficacy against heterologous variants, and the rapid decrease of vaccine-elicited immunity raises serious concerns, calling for improved vaccine tactics. To this end, a pseudovirus nanoparticle (PVNP) displaying the receptor binding domains (RBDs) of SARS-CoV-2 spike, named S-RBD, was generated and shown it as a promising COVID-19 vaccine candidate. The S-RBD PVNP was produced using both prokaryotic and eukaryotic systems. A 3D structural model of the S-RBD PVNPs was built based on the known structures of the S60 particle and RBDs, revealing an S60 particle-based icosahedral symmetry with multiple surface-displayed RBDs that retain authentic conformations and receptor-binding functions. The PVNP is highly immunogenic, eliciting high titers of RBD-specific IgG and neutralizing antibodies in mice. The S-RBD PVNP demonstrated exceptional protective efficacy, and fully (100%) protected K18-hACE2 mice from mortality and weight loss after a lethal SARS-CoV-2 challenge, supporting the S-RBD PVNPs as a potent COVID-19 vaccine candidate. By contrast, a PVNP displaying the N-terminal domain (NTD) of SARS-CoV-2 spike exhibited only 50% protective efficacy. Since the RBD antigens of our PVNP vaccine are adjustable as needed to address the emergence of future variants, and various S-RBD PVNPs can be combined as a cocktail vaccine for broad efficacy, these non-replicating PVNPs offer a flexible platform for a safe, effective COVID-19 vaccine with minimal manufacturing cost and time.

How Innovation Ecosystem Synergy Degree Influences Technology Innovation Performance—Evidence from China’s High-Tech Industry

The technology innovation of high-tech industries has become an important support for the innovation-driven strategy. This study introduces innovation ecosystem synergy as a moderating variable from a systemic and holistic perspective based on the traditional perspective of innovation factor input-output, and helps construct a technology innovation performance driving model based on the Cobb–Douglas knowledge production function, which enriches the discussion perspective and theoretical model research on technology innovation performance. With a sample of 28 provinces in mainland China, this study empirically analyzed the moderating mechanism of innovation performance by innovation synergy in high-tech industries during the two stages of technology development and technology transformation. The findings of the study are as follows: (1) Independent research and development has a positive and significant impact on technology development performance;product innovation has a positive and significant impact on technology transformation performance;(2) Technology introduction can weaken technology development performance due to technology dependence and the inhibitory effect on independent innovation, and inefficient technology renovation can negatively and significantly affect technology transformation performance.;(3) The degree of synergy has a positive and significant impact on the performance of technology development innovation and technology transformation innovation. The degree of synergy has a positive moderating effect on the innovation performance of independent R&D and technology development, as well as product innovation and technology renovation, and a negative moderating effect on the innovation performance of technology introduction and technology development, but no significant moderating effect on technology renovation and technology transformation performance. The research results can provide a reference for the improvement of the technology innovation performance of regional high-tech industries.

Analyzing the ecological relations of technology innovation of the Chinese high-tech industry based on the Lotka-Volterra model.

Technology innovation has become an important driving force of economic and social development and has received wide attention from academics. Most scholars mainly take technology innovation as an overall variable to explore its impact on the economy and society. The main contribution of this study is to open the black box of technology innovation and introduce the lotka-Volterra model to explore the internal structure of technology innovation in the Chinese high-tech industry and to analyze the ecological relationships, evolutionary trends, equilibrium states of six technology innovation species including independent innovation (II), technology import (TI), research & development (RD), technology renovation (TR), foreign technology acquisition (FTA), and domestic technology purchase (DTP). The results of the study show that, First, the ecological relationship between prey and predator is observed between RD and TR, DTP and FTA, and II and TI. Second, no equilibrium state is observed between TD and TF and II and TI. Third, an unstable equilibrium state is observed between RD and TR.

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.

Decreased inhibition of exosomal miRNAs on SARS-CoV-2 replication underlies poor outcomes in elderly people and diabetic patients.

Elderly people and patients with comorbidities are at higher risk of COVID-19 infection, resulting in severe complications and high mortality. However, the underlying mechanisms are unclear. In this study, we investigate whether miRNAs in serum exosomes can exert antiviral functions and affect the response to COVID-19 in the elderly and people with diabetes. First, we identified four miRNAs (miR-7-5p, miR-24-3p, miR-145-5p and miR-223-3p) through high-throughput sequencing and quantitative real-time PCR analysis, that are remarkably decreased in the elderly and diabetic groups. We further demonstrated that these miRNAs, either in the exosome or in the free form, can directly inhibit S protein expression and SARS-CoV-2 replication. Serum exosomes from young people can inhibit SARS-CoV-2 replication and S protein expression, while the inhibitory effect is markedly decreased in the elderly and diabetic patients. Moreover, three out of the four circulating miRNAs are significantly increased in the serum of healthy volunteers after 8-weeks' continuous physical exercise. Serum exosomes isolated from these volunteers also showed stronger inhibitory effects on S protein expression and SARS-CoV-2 replication. Our study demonstrates for the first time that circulating exosomal miRNAs can directly inhibit SARS-CoV-2 replication and may provide a possible explanation for the difference in response to COVID-19 between young people and the elderly or people with comorbidities.

Intra-species sialic acid polymorphism in humans: a common niche for influenza and coronavirus pandemics?

The ongoing COVID-19 pandemic has led to more than 159 million confirmed cases with over 3.3 million deaths worldwide, but it remains mystery why most infected individuals (∼98%) were asymptomatic or only experienced mild illness. The same mystery applies to the deadly 1918 H1N1 influenza pandemic, which has puzzled the field for a century. Here we discuss dual potential properties of the 1918 H1N1 pandemic viruses that led to the high fatality rate in the small portion of severe cases, while about 98% infected persons in the United States were self-limited with mild symptoms, or even asymptomatic. These variations now have been postulated to be impacted by polymorphisms of the sialic acid receptors in the general population. Since coronaviruses (CoVs) also recognize sialic acid receptors and cause severe acute respiratory syndrome epidemics and pandemics, similar principles of influenza virus evolution and pandemicity may also apply to CoVs. A potential common principle of pathogen/host co-evolution of influenza and CoVs under selection of host sialic acids in parallel with different epidemic and pandemic influenza and coronaviruses is discussed.

Deficiency of Tfh Cells and Germinal Center in Deceased COVID-19 Patients.

The COVID-19 pandemic caused by SARS-CoV2 is characterized by a remarkable variation in clinical severity ranging from a mild illness to a fatal multi-organ disease. Understanding the dysregulated human immune responses in the fatal subjects is critical for management of COVID-19 patients and the pandemic. In this study, we examined the immune cell compositions in the lung tissues and hilar lymph nodes using immunohistochemistry on 6 deceased COVID-19 patients and 4 focal organizing pneumonia (FOP) patients who underwent lung surgery and served as controls. We found a dominant presence of macrophages and a general deficiency of T cells and B cells in the lung tissues from deceased COVID-19 patients. In contrast to the FOP patients, Tfh cells and germinal center formation were largely absent in the draining hilar lymph nodes in the deceased COVID-19 patients. This was correlated with reduced IgM and IgG levels compared to convalescent COVID-19 patients. In summary, our data highlight a defect of germinal center structure in deceased COVID-19 patients leading to an impaired humoral immunity. Understanding the mechanisms of this deficiency will be one of the key points for the management of this epidemic.

Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the etiological agent responsible for the global COVID-19 (coronavirus disease 2019) outbreak. The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a pivotal role in mediating viral replication and transcription. We designed and synthesized two lead compounds (11a and 11b) targeting Mpro Both exhibited excellent inhibitory activity and potent anti-SARS-CoV-2 infection activity. The x-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a or 11b, both determined at a resolution of 1.5 angstroms, showed that the aldehyde groups of 11a and 11b are covalently bound to cysteine 145 of Mpro Both compounds showed good pharmacokinetic properties in vivo, and 11a also exhibited low toxicity, which suggests that these compounds are promising drug candidates.

Structure-Based Design, Synthesis and Biological Evaluation of Peptidomimetic Aldehydes as a Novel Series of Antiviral Drug Candidates Targeting the SARS-CoV-2 Main Protease (preprint)

SARS-CoV-2 is the etiological agent responsible for the COVID-19 outbreak in Wuhan. Specific antiviral drug are urgently needed to treat COVID-19 infections. The main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, which makes it an attractive drug target. In an effort to rapidly discover lead compounds targeting Mpro, two compounds (11a and 11b) were designed and synthesized, both of which exhibited excellent inhibitory activity with an IC50 value of 0.05 M and 0.04 M respectively. Significantly, both compounds exhibited potent anti-SARS-CoV-2 infection activity in a cell-based assay with an EC50 value of 0.42 M and 0.33 M, respectively. The X-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a and 11b were determined at 1.5 [A] resolution, respectively. The crystal structures showed that 11a and 11b are covalent inhibitors, the aldehyde groups of which are bound covalently to Cys145 of Mpro. Both compounds showed good PK properties in vivo, and 11a also exhibited low toxicity which is promising drug leads with clinical potential that merits further studies.