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Summary: With the rapid emergence and spread of SARS-CoV-2 variants, development of vaccines with broad and potent protectivity has become a global priority. Here, we designed a lipid nanoparticle-encapsulated, nucleoside-unmodified mRNA (mRNA-LNP) vaccine encoding the trimerized receptor-binding domain (RBD trimer) and showed its robust capability in inducing broad and protective immune responses against wild-type and major variants of concern (VOCs) in the mouse model of SARS-CoV-2 infection. The protectivity was correlated with RBD-specific B cell responses especially the long-lived plasma B cells in bone marrow, strong ability in triggering BCR clustering, and downstream signaling. Monoclonal antibodies isolated from vaccinated animals demonstrated broad and potent neutralizing activity against VOCs tested. Structure analysis of one representative antibody identified a novel epitope with a high degree of conservation among different variants. Collectively, these results demonstrate that the RBD trimer mRNA vaccine serves as a promising vaccine candidate against SARS-CoV-2 variants and beyond.

BackgroundNeuropathic pain (NP) is a debilitating and refractory chronic pain with a higher prevalence especially in elderly patients. Cell senescence considered a key pathogenic factor in NP. The objective of this research is to discover genes associated with aging in peripheral blood of individuals with NP using bioinformatics techniques.MethodsTwo cohorts (GSE124272 and GSE150408) containing peripheral blood samples of NP were downloaded from the GEO database. By merging the two cohorts, differentially expressed aging-related genes (DE-ARGs) were obtained by intersection with aging-related genes. The potential biological mechanisms of DE-ARGs were further analyzed through GO and KEGG. Three machine learning methods, namely, LASSO, SVM-RFE, and Random Forest, were utilized to identify diagnostic biomarkers. A Nomogram model was developed to assess their diagnostic accuracy. The validation of biomarker expression and diagnostic effectiveness was conducted in three distinct pain cohorts. The CIBERSORT algorithm was employed to evaluate the immune cell composition in the peripheral blood of patients with NP and investigate its association with the expression of diagnostic biomarkers.ResultsThis study identified a total of 24 DE-ARGs, mainly enriched in “Chemokine signaling pathway,” “Inflammatory mediator regulation of TRP channels,” “HIF-1 signaling pathway” and “FOXO signaling pathway”. Three machine learning algorithms identified a total of four diagnostic biomarkers (CEBPA, CEACAM1, BTG3 and IL-1R1) with good diagnostic performance and the similar expression difference trend in different types of pain cohorts. The expression levels of CEACAM1 and IL-1R1 exhibit a positive correlation with the percentage of neutrophils.ConclusionUsing machine learning techniques, our research identified four diagnostic biomarkers related to aging in peripheral blood, providing innovative approaches for the diagnosis and treatment of NP.

Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients.Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases.Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to “macroautophagy” and “autophagy-animal,” DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts.Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.

BackgroundThe aim of this study was to investigate the functional abnormalities between the nucleus accumbens (NAc) and the whole brain in individuals with Insomnia Disorder (ID) using resting-state functional magnetic resonance imaging (fMRI). Additionally, the study aimed to explore the underlying neural mechanisms of ID.MethodsWe enrolled 18 participants with ID and 16 normal controls (NC). Resting-state functional connectivity (FC) between the NAc and the whole brain voxels was calculated and compared between the two groups to identify differential brain region. Receiver operating characteristic (ROC) curve analysis was employed to assess the ability of differential features to distinguish between groups. Furthermore, Pearson correlation analysis was performed to examine the relationship between neurocognitive scores and differential features.ResultsThe ID group exhibited significantly reduced FC values in several brain regions, including the right supplementary motor area, the bilateral middle frontal gyrus, the bilateral median cingulate and paracingulate gyri and the left precuneus. The area under the curve (AUC) of the classification model based on FC in these brain regions was 83.3%. Additionally, the abnormal functional changes observed in ID patients were positively correlated with the Fatigue Severity Scale (R = 0.650, p = 0.004).ConclusionThese findings suggest that the NAc may play a crucial role in the diagnosis of ID and could serve as a potential imaging biomarker, providing insights into the underlying neural mechanisms of the disorder.

The physicochemical stimulation of acupoints is a widespread treatment strategy for different diseases, such as sciatica. Its efficacy is mainly based on the temporal and spatial modulation of the physicochemical properties of the acupoints. The existing therapies based on the stimulation of acupoints have certain disadvantages. Therefore, in this study, injectable dexamethasone (DXM)- and magnetic Fe 3 O 4 nanoparticles-loaded chitosan/β-glycerophosphate (CS/GP) thermal crosslinking hydrogels were prepared, thereby improving the performance of embedding materials. The sciatica rat models were established to compare the therapeutic effects of hydrogels and catgut. The DXM or Fe 3 O 4 -loaded CS/GP hydrogels were compared in terms of their gelation kinetics, release kinetics, magnetic responsiveness in-vitro , and biocompatibility as well as their analgesic effects on the chronic constriction injury of the sciatic nerve (CCI) rats in-vivo . The CS/GP/Fe 3 O 4 /DXM hydrogel showed comparable gelation kinetics and good magnetic responsiveness in-vitro . This hydrogel could relieve sciatica by reducing the expression levels of inflammatory factors in serum, inhibiting the p38MAPK (p38, mitogen-activated protein kinase) phosphorylation, and decreasing the expression level of the P2X4 receptor (P2X4R) in the spinal dorsal horn. In conclusion, the DXM or Fe 3 O 4 -loaded CS/GP hydrogels can be considered as a treatment option for the physiochemical stimulation therapy of acupoints to improve sciatica.

This article starts with the environmental changes in human cognition, analyzes the virtual as the main feature of visual perception under digital technology, and explores the transition from passive to active human cognitive activities. With the diversified understanding of visual information, human contradiction of memory also began to become prominent. Aiming at the problem that the existing multimodal TV media recognition methods have low recognition rate of unknown application layer protocols, an adaptive clustering method for identifying unknown application layer protocols is proposed. This method clusters application layer protocols based on similarity of the load characteristics of network stream application layer protocol data. The method divides the similarity calculation in the clustering algorithm to improve the clustering efficiency of the algorithm. Experimental results show that the proposed method can efficiently and accurately recognize unknown visual communication. This article proposes that, in the interactive multimodal visual information transmission, human visual perception experience has changed, the diversity of visual information content expression makes the aesthetic subject more personalized and stylized.