Management for stroke intelligent early warning empowered by big data.

Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.

A bivalent subunit vaccine efficiently produced in Pichia pastoris against SARS-CoV-2 and emerging variants.

The emergence of severe acute respiratory syndrome coronavirus type II (SARS-CoV-2) variants have led to a decline in the protection of existing vaccines and antibodies, and there is an urgent need for a broad-spectrum vaccination strategy to reduce the pressure on the prevention and control of the pandemic. In this study, the receptor binding domain (RBD) of the SARS-CoV-2 Beta variant was successfully expressed through a glycoengineered yeast platform. To pursue a more broad-spectrum vaccination strategy, RBD-Beta and RBD-wild type were mixed at the ratio of 1:1 with Al(OH)3 and CpG double adjuvants for the immunization of BALB/c mice. This bivalent vaccine stimulated robust conjugated antibody titers and a broader spectrum of neutralizing antibody titers. These results suggested that a bivalent vaccine of RBD-Beta and RBD-wild type could be a possible broad-spectrum vaccination strategy.

Severe Anemia Observed in a Chinese Patient with COVID-19 and Plasmodium Falciparum Malaria Co-Infection.

BACKGROUND: COVID-19 and malaria share some similar symptoms such as fever, difficulty in breathing, fatigue, and headaches of acute onset. With overlapping symptoms and travel history significant for COVID-19 and malaria, healthcare systems and professionals will face a great challenge in the case of COVID-19 and malaria co-infection. METHODS: Here we presented a patient with COVID-19 infection and refractory anemia of unknown reason. A diagnostic test for malaria was later performed. RESULTS: The patient was ultimately diagnosed with COVID-19 and plasmodium falciparum malaria co-infection. He recovered gradually after receiving anti-malaria treatment. CONCLUSIONS: The present case highlights the danger of focusing only on a diagnosis of COVID-19, reminding clinicians to be vigilant about the possibility of co-infections.

Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring

Summary Monitoring respiration is vital for personal diagnosis of chronic diseases. However, the existing respiratory sensors have severe limitations, such as single function, finite detection parameters, and lack of smart signal analysis. Here, we present an integrated wearable and low-cost smart respiratory monitoring sensor (RMS) system with artificial intelligence (AI)-assisted diagnosis of respiratory abnormality by detecting multi-parameters of human respiration. Coupling with intelligent analysis and data mining algorithms embedded in a phone app, the lighter system of 7.3 g can acquire real-time self-calibrated parameters, including breathing frequency, apnea hypopnea index (AHI), vital capacity (VC), peak expiratory flow (PEF), and other respiratory indexes with an accuracy >95.21%. The data can be wirelessly transferred to the user's data cloud terminal. The RMS system enables comprehensive multi-physiological parameters analysis for auxiliary diagnosing and classifying diseases, including sleep apnea, rhinitis, and chronic lung diseases, as well as rehabilitation of COVID-19, and exhibits advantages of portable healthcare.

A Vaccine with Multiple Receptor-Binding Domain Subunit Mutations Induces Broad-Spectrum Immune Response against SARS-CoV-2 Variants of Concern.

With the emergence of more variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the immune evasion of these variants from existing vaccines, the development of broad-spectrum vaccines is urgently needed. In this study, we designed a novel SARS-CoV-2 receptor-binding domain (RBD) subunit (RBD5m) by integrating five important mutations from SARS-CoV-2 variants of concern (VOCs). The neutralization activities of antibodies induced by the RBD5m candidate vaccine are more balanced and effective for neutralizing different SARS-CoV-2 VOCs in comparison with those induced by the SARS-CoV-2 prototype strain RBD. Our results suggest that the RBD5m vaccine is a good broad-spectrum vaccine candidate able to prevent disease from several different SARS-CoV-2 VOCs.

Highly Efficient Red/NIR-Emissive Fluorescent Probe with Polarity-Sensitive Character for Visualizing Cellular Lipid Droplets and Determining Their Polarity.

Lipid droplets (LDs), which are ubiquitous organelles existing in almost all eukaryotic cells, have attracted a lot of attention in the field of cell biology over the last decade. For the biological study of LDs via fluorescence imaging, the superior LD fluorescent probes with environmental polarity-sensitive character are highly desired and powerful but are very scarce. Herein, we have newly developed such a kind of fluorescent probe named LDs-Red which enables us to visualize LDs and to further reveal their polarity information. This fluorescent probe displays the advantages of intense red/near-infrared emission, high LD staining specificity, and good photostability; thus, it would be very useful for LD fluorescence imaging application. As a result, the three-dimensional confocal imaging to visualize spatial distribution of LDs and the multicolor confocal imaging to simultaneously observe LDs and other cellular organelles have been realized using this new LD fluorescent probe. Furthermore, the polarity-sensitive emission character of this probe enables us to quantitatively determine the LD polarity via spectral scan imaging. Consequently, the cancer cells (HepG2, HeLa, and Panc02) displaying lower polarity of LDs than the normal cells (L929, U251, and HT22) have been systematically demonstrated. In addition, this polarity-sensitive probe displaying shorter fluorescence wavelengths in cancer cells than in normal cells has an important and potential ability to distinguish them.

Mortality and risk factors for COVID-19 in hemodialysis patients: A systematic review and meta-analysis.

Introduction: The present study systematically reviewed the clinical features and risk factors in patients undergoing maintenance hemodialysis (MHD) who also acquired coronavirus disease 2019 (COVID-19). More specifically, clinical manifestations, prognosis, and risk factors for death among this population were explored. Method: A literature search using the PubMed, Web of Science, and Embase databases, for articles involving patients with laboratory-confirmed COVID-19 and undergoing MHD published between January 1, 2020, and March 13, 2022, was performed. Random-effects meta-analyses were performed to calculate the weighted mean prevalence and corresponding 95% confidence interval (CI) or weighted means and 95% CI. Heterogeneity among studies was assessed using I2 statistics. Results: Twenty-two studies including 13,191 patients with COVID-19 undergoing MHD were selected. The most common symptoms included fever (53% [95% CI 41%-65%]) and cough (54% [95% CI 48%-60%]); however, 17% (95% CI 11%-22%) of the cases were asymptomatic. In subgroup analysis, the proportion of male patients (65% [95% CI 58%-71%]), and patients with coronary artery disease (30% [95% CI 17%-44%) and chronic obstructive pulmonary disease (9% [95% CI 4%-15%]) was greater in the non-survivor group compared with the survivor group. Furthermore, patients undergoing MHD, who were also positive for COVID-19, exhibited a high mortality rate (24% [95% CI 19%-28%]). Conclusions: MHD patients with COVID-19 may initially present as asymptomatic or with mild symptoms; nevertheless, in this study, these patients exhibited a higher risk for death compared with COVID-19 patients not undergoing MHD. Moreover, male sex and underlying cardiovascular and respiratory diseases increased the mortality risk.

Factors Influencing Delayed Treatment in Patients With Breast Cancer During COVID-19 Pandemic.

Background: The outbreak of coronavirus disease 2019 (COVID-19) has endangered human health and life. This pandemic has changed people's lifestyle and affected the regular delivery of standard cancer treatment. In the present study, we aimed to explore the influencing factors of delayed treatment in patients with breast cancer during COVID-19 pandemic. Methods: This study was a cross-sectional investigation, and the subjects were patients who were discharged from the department of burn and plastic surgery after February 2020. All participants completed this study's online questionnaire based on the WeChat and Wenjuanxing platforms. Levels of anxiety and depression were measured by the Hospital Anxiety and Depression Scale (HADS). Patients were divided into a delay group and non-delay group according to the occurrence of delayed treatment. Univariate analysis was performed by using the t test or chi-square test. A logistic regression model was employed to determine factors associated with delayed treatment. Results: The present study included a total of 397 patients with breast cancer, among whom delayed treatment occurred in 76 patients, accounting for 19.1%. Scores on both the anxiety subscale and depression subscale in delay group were significantly higher than those in non-delay group. Compared with non-delay group, we found that patients in delay group usually had a higher level of education (P = 0.020), worse self-feeling (P = 0.030), poor compliance of medical order (P = 0.042), and a higher prevalence of anxiety (P = 0.004) and depression (P = 0.012). Traffic inconvenience was also an important relevant factor for delayed treatment (P = 0.001). The prevalence of recurrence in delay group was higher than that in non-delay group (P = 0.018). By using logistic multivariate regression analysis, the results revealed that level of education and traffic inconvenience were independent factors influencing delayed treatment in patients with breast cancer during COVID-19 pandemic. Conclusion: The prevalence of delayed treatment in patients with breast cancer during COVID-19 pandemic is relatively high. Our findings reveal several influencing factors closely associated with delayed treatment, which is useful information that will be beneficial for patients to receive standardized therapy by taking targeted measures.

Learning COVID-19 Pneumonia Lesion Segmentation From Imperfect Annotations via Divergence-Aware Selective Training.

Automatic segmentation of COVID-19 pneumonia lesions is critical for quantitative measurement for diagnosis and treatment management. For this task, deep learning is the state-of-the-art method while requires a large set of accurately annotated images for training, which is difficult to obtain due to limited access to experts and the time-consuming annotation process. To address this problem, we aim to train the segmentation network from imperfect annotations, where the training set consists of a small clean set of accurately annotated images by experts and a large noisy set of inaccurate annotations by non-experts. To avoid the labels with different qualities corrupting the segmentation model, we propose a new approach to train segmentation networks to deal with noisy labels. We introduce a dual-branch network to separately learn from the accurate and noisy annotations. To fully exploit the imperfect annotations as well as suppressing the noise, we design a Divergence-Aware Selective Training (DAST) strategy, where a divergence-aware noisiness score is used to identify severely noisy annotations and slightly noisy annotations. For severely noisy samples we use an regularization through dual-branch consistency between predictions from the two branches. We also refine slightly noisy samples and use them as supplementary data for the clean branch to avoid overfitting. Experimental results show that our method achieves a higher performance than standard training process for COVID-19 pneumonia lesion segmentation when learning from imperfect labels, and our framework outperforms the state-of-the-art noise-tolerate methods significantly with various clean label percentages.

Non-glycosylated SARS-CoV-2 RBD elicited a robust neutralizing antibody response in mice.

The glycosylated receptor-binding domain (glycoRBD) of SARS-CoV-2 can induce protective neutralizing antibodies to function as a vaccine. However, it is unclear whether vaccines using non-glycosylated RBD (non-glycoRBD) can induce protective immunity. Here, we report the efficacy of a SARS-CoV-2 non-glycoRBD vaccine produced by prokaryotic system in mice. The recombinant non-glycoRBD protein was overexpressed in Escherichia coli in the form of inclusion bodies, and was obtained after renaturation and three-step purification. From HPLC analysis, the purity of the RBD was 99%. Additionally, angiotensin converting enzyme 2 (ACE2)-binding assays revealed that E.coli-derived non-glycoRBD had binding activity consistent with glycoRBD. The RBD was formulated with CpG ODN and Al(OH)3 adjuvants and the obtained RBD candidate vaccine elicited potent antibody responses and neutralized SARS-CoV-2 wild-type, Delta, and Omicron pseudoviruses. In summary, our data showed that a non-glycoRBD candidate vaccine produced by E.coli provided a robust immune response and had pseudovirus neutralizing activity, making it a solid candidate vaccine for protection against SARS-CoV-2.

Construction of Orthogonal Modular Proteinaceous Nanovaccine Delivery Vectors Based on mSA-Biotin Binding.

Proteinaceous nanovaccine delivery systems have significantly promoted the development of various high-efficiency vaccines. However, the widely used method of coupling the expression of scaffolds and antigens may result in their structural interference with each other. Monovalent streptavidin (mSA) is a short monomer sequence, which has a strong affinity for biotin. Here, we discuss an orthogonal, modular, and highly versatile self-assembled proteinaceous nanoparticle chassis that facilitates combinations with various antigen cargos by using mSA and biotin to produce nanovaccines. We first improved the yield of these nanoparticles by appending a short sugar chain on their surfaces in a constructed host strain. After confirming the strong ability to induce both Th1- and Th2-mediated immune responses based on the plasma cytokine spectrum from immunized mice, we further verified the binding ability of biotinylated nanoparticles to mSA-antigens. These results demonstrate that our biotinylated nanoparticle chassis could load both protein and polysaccharide antigens containing mSA at a high affinity. Our approach thus offers an attractive technology for combining nanoparticles and antigen cargos to generate various high-performance nanovaccines. In particular, the designed mSA connector (mSA containing glycosylation modification sequences) could couple with polysaccharide antigens, providing a new attractive strategy to prepare nanoscale conjugate vaccines.

Kappa-RBD produced by glycoengineered Pichia pastoris elicited high neutralizing antibody titers against pseudoviruses of SARS-CoV-2 variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kappa (B.1.617.1) variant represented the main variant of concern (VOC) for the epidemic in India in May 2021. We have previously established a technology platform for rapidly preparing SARS-CoV-2 receptor-binding domain (RBD) candidate vaccines based on glycoengineered Pichia pastoris. Our previous study revealed that the wild-type RBD (WT-RBD) formulated with aluminum hydroxide and CpG 2006 adjuvant effectively induces neutralizing antibodies in BALB/c mice. In the present study, a glycoengineered P. pastoris expression system was used to prepare recombinant kappa-RBD candidate vaccine. Kappa-RBD formulated with CpG and alum induced BALB/c mice to produce a potent antigen-specific antibody response and neutralizing antibody titers against pseudoviruses of SARS-CoV-2 kappa, delta, lambda, beta, and omicron variants and WT. Therefore, the recombinant kappa-RBD vaccine has sufficient potency to be a promising COVID-19 vaccine candidate.

Impact of Gender Difference on Anxiety in COVID-19 Patients in Quarantine Wards.

OBJECTIVE: This study aimed to investigate the gender difference in anxiety in novel coronavirus pneumonia (COVID-19) patients in the quarantine ward during the outbreak. METHODS: The self-rating anxiety scale (SAS) was used on the seventh day of isolation to analyze the anxiety levels of a total of 242 suspected or confirmed COVID-19 patients in the quarantine wards of two hospitals; 232 of these patients (112 males and 120 females) completed the anxiety scoring. The anxiety scores were compared between male and female patients using the t-test, and a scatter diagram was used for analysis. RESULTS: The SAS scores of females in quarantine wards were higher than those of males at seven days of isolation. CONCLUSION: Women with COVID-19 are more prone to anxiety than men while in isolation. It is necessary to give more attention to female patients in quarantine wards in the future, and psychological counseling may be necessary.

Cardiovascular Risk After SARS-CoV-2 Infection Is Mediated by IL18/IL18R1/HIF-1 Signaling Pathway Axis.

Objectives: Currently, cardiovascular risk associated with COVID-19 has been brought to people's attention, but the mechanism is not clear. The aim of this study is to elucidate the mechanisms based on multiple omics data. Methodology: Weighted gene co-expression network analysis (WGCNA) was used to identify key pathways. Combination analysis with aneurysm and atherosclerosis related pathways, hypoxia induced factor-1 (HIF-1) signaling were identified as key pathways of the increased cardiovascular risk associated with COVID-19. ScMLnet algorithm based on scRNA-seq was used to explore the regulation of HIF-1 pathway by intercellular communication. Proteomic analysis was used to detect the regulatory mechanisms between IL18 and HIF-1 signaling pathway. Pseudo time locus analysis was used to study the regulation of HIF1 signaling pathway in macrophages and vascular smooth muscle cells (VSMC) phenotypic transformation. The Virtual Inference of protein-activity by Enriched Regulon (VIPER) analysis was used to study the activity of regulatory proteins. Epigenetic analysis based on methylation revealed epigenetic changes in PBMC after SARS-CoV-2 infection. Potential therapeutic compounds were explored by using Cmap algorithm. Results: HIF-1 signaling pathway is a common key pathway for aneurysms, atherosclerosis and SARS-CoV-2 infection. Intercellular communication analysis showed that macrophage-derived interleukin-18 (IL-18) activates the HIF-1 signaling pathway through IL18R1. Proteomic analysis showed that IL18/IL18R1 promote NF-κB entry into the nucleus, and activated the HIF-1 signaling pathway. Macrophage-derived IL18 promoted the M1 polarization of macrophages and the syntactic phenotype transformation of VSMCs. MAP2K1 mediates the functional regulation of HIF-1 signaling pathway in various cell types. Epigenetic changes in PBMC after COVID-19 infection are characterized by activation of the type I interferon pathway. MEK inhibitors are the promising compounds for the treatment of HIF-1 overactivation. Conclusions: The IL18/IL18R1/HIF1A axis is expected to be an therapeutic target for cardiovascular protection after SARS-CoV-2 infection. MEK inhibitors may be an choice for cardiovascular protection after SARS-COV-2 infection.

The association of obesity with the progression and outcome of COVID-19: The insight from an artificial-intelligence-based imaging quantitative analysis on computed tomography.

AIMS: To explore the association of obesity with the progression and outcome of coronavirus disease 2019 (COVID-19) at the acute period and 5-month follow-up from the perspectives of computed tomography (CT) imaging with artificial intelligence (AI)-based quantitative evaluation, which may help to predict the risk of obese COVID-19 patients progressing to severe and critical disease. MATERIALS AND METHODS: This retrospective cohort enrolled 213 hospitalized COVID-19 patients. Patients were classified into three groups according to their body mass index (BMI): normal weight (from 18.5 to <24 kg/m2 ), overweight (from 24 to <28 kg/m2 ) and obesity (≥28 kg/m2 ). RESULTS: Compared with normal-weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 175.5[34.0-414.9] vs. 261.7[73.3-576.2] vs. 395.8[101.6-1135.6]; p = 0.002), and were more inclined to deterioration at the acute period. At the 5-month follow-up, the lung residual lesion was more serious (residual total lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 4.8[0.0-27.4] vs. 10.7[0.0-55.5] vs. 30.1[9.5-91.1]; p = 0.015), and the absorption rates were lower for higher BMI patients (absorption rates of total lung lesions volume [%], normal weight vs. overweight vs. obesity; 99.6[94.0-100.0] vs. 98.9[85.2-100.0] vs. 88.5[66.5-95.2]; p = 0.013). The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration (areas under the ROC curve, 0.884 vs. 0.794, p < 0.05). CONCLUSIONS: Obesity was associated with severe pneumonia lesions on CT and adverse clinical outcomes. The AI-based model with combinational use of clinical and CT parameters had incremental prognostic value over the clinical parameters alone.

Development and validation of prognosis model of mortality risk in patients with COVID-19.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

Efficacy of Qingfei Paidu Decoction on Patients with COVID-19 Pneumonia in Wuhan, China: A Propensity Score Matching Study.

BACKGROUND: In view of the global efforts to develop effective treatments for the current worldwide coronavirus 2019 (COVID-19) pandemic, Qingfei Paidu decoction (QPD), a novel traditional Chinese medicine (TCM) prescription, was formulated as an optimized combination of constituents of classic prescriptions used to treat numerous febrile and respiratory-related diseases. This prescription has been used to treat patients with COVID-19 pneumonia in Wuhan, China. Hypothesis/Purpose. We hypothesized that QPD would have beneficial effects on patients with COVID-19. We aimed to prove this hypothesis by evaluating the efficacy of QPD in patients with COVID-19 pneumonia. METHODS: In this single-center, retrospective, observational study, we identified eligible participants who received a laboratory diagnosis of COVID-19 between January 15 and March 15, 2020, in the west campus of Union Hospital in Wuhan, China. QPD was supplied as an oral liquid packaged in 200-mL containers, and patients were orally administered one package twice daily 40 minutes after a meal. The primary outcome was death, which was compared between patients who did and did not receive QPD (QPD and NoQPD groups, respectively). Propensity score matching (PSM) was used to identify cohorts. RESULTS: In total, 239 and 522 participants were enrolled in the QPD and NoQPD groups, respectively. After PSM at a 1 : 1 ratio, 446 patients meeting the criteria were included in the analysis with 223 in each arm. In the QPD and NoQPD groups, 7 (3.2%) and 29 (13.0%) patients died, and those in the QPD group had a significantly lower risk of death (hazard ratio (HR) 0.29, 95% CI: 0.13-0.67) than those in the NoQPD group (p = 0.004). Furthermore, the survival time was significantly longer in the QPD group than in the NoQPD group (p < 0.001). CONCLUSION: The use of QPD may reduce the risk of death in patients with COVID-19 pneumonia.

Nonlinear response of nitrate to NOx reduction in China during the COVID-19 pandemic.

In recent years, nitrate plays an increasingly important role in haze pollution and strict emission control seems ineffective in reducing nitrate pollution in China. In this study, observations of gaseous and particulate pollutants during the COVID-19 lockdown, as well as numerical modelling were integrated to explore the underlying causes of the nonlinear response of nitrate mitigation to nitric oxides (NOx) reduction. We found that, due to less NOx titration effect and the transition of ozone (O3) formation regime caused by NOx emissions reduction, a significant increase of O3 (by ∼ 69%) was observed during the lockdown period, leading to higher atmospheric oxidizing capacity and facilitating the conversion from NOx to oxidation products like nitric acid (HNO3). It is proven by the fact that 26-61% reduction of NOx emissions only lowered surface HNO3 by 2-3% in Hebi and Nanjing, eastern China. In addition, ammonia concentration in Hebi and Nanjing increased by 10% and 40% during the lockdown, respectively. Model results suggested that the increasing ammonia can promote the gas-particle partition and thus enhance the nitrate formation by up to 20%. The enhanced atmospheric oxidizing capacity together with increasing ammonia availability jointly promotes the nitrate formation, thereby partly offsetting the drop of NOx. This work sheds more lights on the side effects of a sharp NOx reduction and highlights the importance of a coordinated control strategy.

Biosynthesis of Self-Assembled Proteinaceous Nanoparticles for Vaccination.

Recent years have seen enormous advances in nanovaccines for both prophylactic and therapeutic applications, but most of these technologies employ chemical or hybrid semi-biosynthetic production methods. Thus, production of nanovaccines has to date failed to exploit biology-only processes like complex sequential post-translational biochemical modifications and scalability, limiting the realization of the initial promise for offering major performance advantages and improved therapeutic outcomes over conventional vaccines. A Nano-B5 platform for in vivo production of fully protein-based, self-assembling, stable nanovaccines bearing diverse antigens including peptides and polysaccharides is presented here. Combined with the self-assembly capacities of pentamer domains from the bacterial AB5 toxin and unnatural trimer peptides, diverse nanovaccine structures can be produced in common Escherichia coli strains and in attenuated pathogenic strains. Notably, the chassis of these nanovaccines functions as an immunostimulant. After showing excellent lymph node targeting and immunoresponse elicitation and safety performance in both mouse and monkey models, the strong prophylactic effects of these nanovaccines against infection, as well as their efficient therapeutic effects against tumors are further demonstrated. Thus, the Nano-B5 platform can efficiently combine diverse modular components and antigen cargos to efficiently generate a potentially very large diversity of nanovaccine structures using many bacterial species.

A Vaccine Based on the Receptor-Binding Domain of the Spike Protein Expressed in Glycoengineered Pichia pastoris Targeting SARS-CoV-2 Stimulates Neutralizing and Protective Antibody Responses.

In 2020 and 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, caused a global pandemic. Vaccines are expected to reduce the pressure of prevention and control, and have become the most effective strategy to solve the pandemic crisis. SARS-CoV-2 infects the host by binding to the cellular receptor angiotensin converting enzyme 2 (ACE2) via the receptor-binding domain (RBD) of the surface spike (S) glycoprotein. In this study, a candidate vaccine based on a RBD recombinant subunit was prepared by means of a novel glycoengineered yeast Pichia pastoris expression system with characteristics of glycosylation modification similar to those of mammalian cells. The candidate vaccine effectively stimulated mice to produce high-titer anti-RBD specific antibody. Furthermore, the specific antibody titer and virus-neutralizing antibody (NAb) titer induced by the vaccine were increased significantly by the combination of the double adjuvants Al(OH)3 and CpG. Our results showed that the virus-NAb lasted for more than six months in mice. To summarize, we have obtained a SARS-CoV-2 vaccine based on the RBD of the S glycoprotein expressed in glycoengineered Pichia pastoris, which stimulates neutralizing and protective antibody responses. A technical route for fucose-free complex-type N-glycosylation modified recombinant subunit vaccine preparation has been established.