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Neuromyelitis optica spectrum disorder (NMOSD) is a rare, disabling inflammatory disease of the central nervous system (CNS). Aquaporin-4 (AQP4)-specific T cells play a key role in the pathogenesis of NMOSD. In addition to immune factors, T cells recognizing the AQP4 epitope showed cross-reactivity with homologous peptide sequences in C. perfringens proteins, suggesting that the gut microbiota plays an integral role in the pathogenicity of NMOSD. In this review, we summarize research on the involvement of the gut microbiota in the pathophysiology of NMOSD and its possible pathogenic mechanisms. Among them, Clostridium perfringens and Streptococcus have been confirmed to play a role by multiple studies. Based on this evidence, metabolites produced by gut microbes, such as short-chain fatty acids (SCFAs), tryptophan (Trp), and bile acid (BA) metabolites, have also been found to affect immune cell metabolism. Therefore, the role of the gut microbiota in the pathophysiology of NMOSD is very important. Alterations in the composition of the gut microbiota can lead to pathological changes and alter the formation of microbiota-derived components and metabolites. It can serve as a biomarker for disease onset and progression and as a potential disease-modifying therapy. [ABSTRACT FROM AUTHOR]

Background: The axonal growth capacity of retinal ganglion cells decreases dramatically within the first day of birth, and the axonal regeneration after injury in mature mammals is very limited. Here, this study aimed to delineate the transcriptomic changes associated with altered axonal growth capacity and to identify the key genes associated with axonal regeneration by the RNA sequencing (RNA-Seq) analysis. Methods: The whole retinas from the mice of embryonic day (E) 20, postnatal day (P) 1 and P3 were collected at 6 hours after optic nerve crush (ONC). Differentially expressed genes (DEGs) for ONC or ages were identified by the RNA-Seq analysis. K-means analysis was conducted for the clustering of DEGs based on expression patterns. Enrichment of functions and signaling pathways analysis were performed based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene Set Enrichment analysis (GSEA). Quantitative real time polymerase chain reaction (qRT-PCR) was used to validate the DEGs selected from the RNA-Seq analysis. Results: In total, 5,408 DEGs were identified for ages, and 2,639 DEGs in neonatal mouse retina after ONC. K-means analysis revealed 7 clusters in age-DEGs and 11 clusters in ONC-DEGs. The GO, KEGG and GSEA pathway analyses identified significantly enrichment of DEGs in the visual perception and phototransduction for the age effect, and the break repair, neuron projection guidance, and immune system pathway for the ONC. PPI analysis identified hub genes in the axon-related gene cluster. The expressions of Mlc1, Zfp296, Atoh7, Ecel1, Creb5, Fosb, and Lcn2, thought to be involved in RGC death and axonal growth were validated by qRT-PCR. Conclusions: This study, for the first time, delineated the gene expression changes following ON injury in embryonic and neonatal mice, providing a new resource of age- and injury-driven data on axonal growth capacity. [ABSTRACT FROM AUTHOR]

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks., Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny., Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation., Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks., Competing Interests: LX, ML, and YH were employed by Hybribio Medical Laboratory Group Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yu, Tan, Tang, Zhao, Ji, Lin, He, Gu, Liang, Wang, Chen, Yang, Xie, Wang, Liu, He, Chen, Wang, Wu, Zhao, Liu, Wang, Hao, Cen, Yao, Zhang, Liu, Lye, Hao, Wong and Cen.)

Intraocular pressure elevation can induce retinal ganglion cell death and is a clinically reversible risk factor for glaucoma, the leading cause of irreversible blindness. We previously demonstrated that casein kinase-2 inhibition can promote retinal ganglion cell survival and axonal regeneration in rats after optic nerve injury. To investigate the underlying mechanism, in the current study we increased the intraocular pressure of adult rats to 75 mmHg for 2 hours and then administered a casein kinase-2 inhibitor (4,5,6,7-tetrabromo-2-azabenzimidazole or 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole) by intravitreal injection. We found that intravitreal injection of 4,5,6,7-tetrabromo-2-azabenzimidazole or 2-dimethylamino-4,5,6,7-tetrabromo- 1 H-benzimidazole promoted retinal ganglion cell survival and reduced the number of infiltrating macrophages. Transcriptomic analysis showed that the mitogen activated protein kinase signaling pathway was involved in the response to intraocular pressure elevation but was not modulated by the casein kinase-2 inhibitors. Furthermore, casein kinase-2 inhibition downregulated the expression of genes (Cck, Htrsa, Nef1, Htrlb, Prph, Chat, Slc18a3, Slc5a7, Scn1b, Crybb2, Tsga10ip, and Vstm21) involved in intraocular pressure elevation. Our data indicate that inhibition of casein kinase-2 can enhance retinal ganglion cell survival in rats after acute intraocular pressure elevation via macrophage inactivation., Competing Interests: None

The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure prediction. Both Alphafold2 and RoseTTAFold algorithms were built locally using the open-source codebases. A dataset with 48 protein structures from Protein Data Bank (PDB) was adopted for algorithm set-up validation. A website was built to match ocular genes with the corresponding predicted tertiary protein structures for each amino acid sequence. The predicted local distance difference test-Cα (pLDDT) and template modeling (TM) scores of the validation protein structure and the selected ocular genes were evaluated. Molecular dynamics and molecular docking simulations were performed to demonstrate the applications of the predicted structures. For the validation dataset, 70.8% of the predicted protein structures showed pLDDT greater than 90. Compared to the PDB structures, 100% of the AlphaFold2-predicted structures and 97.9% of the RoseTTAFold-predicted structure showed TM score greater than 0.5. Totally, 1329 amino acid sequences of 430 ocular disease-related genes have been predicted, of which 75.9% showed pLDDT greater than 70 for the wildtype sequences and 76.1% for the variant sequences. Small molecule docking and molecular dynamics simulations revealed that the predicted protein structures with higher confidence scores showed similar molecular characteristics with the structures from PDB. We have developed an ocular protein structure database (EyeProdb) for ocular disease, which is released for the public and will facilitate the biological investigations and structure-based drug development for ocular diseases. Database URL:  http://eyeprodb.jsiec.org., (© The Author(s) 2023. Published by Oxford University Press.)

Spatio-temporal distribution of PM 2.5 and variations in the relationship between PM 2.5 and other pollutants are the main components of PM 2.5 spatio-temporal statistical analysis. Existing methods directly analyze spatio-temporal distribution based on monitoring data; thus, it is difficult to effectively reveal the aggregation structure of PM 2.5 concentrations. Geographically weighted regression, commonly used to model the relationships between PM 2.5 and other pollutants, cannot accurately describe the spatio-temporal variability of dependency. In this study, the clustering structure of PM 2.5 concentrations in Beijing was identified using the spatial clustering algorithm and the seasonal distribution characteristics of PM 2.5 were analyzed based on the clustering results. The relationship between PM 2.5 and PM 10 was modeled by geographically and temporally weighted regression and the spatio-temporal variability of dependency was analyzed according to the regression results. The results showed that PM 2.5 pollution levels and spatial variability were lower in spring and summer than those in autumn and winter and the concentration of PM 2.5 in each season was characterized by low spatial distribution in the north and high spatial distribution in the south. Geographically and temporally weighted regression showed better performance; the correlations between PM 2.5 and PM 10 in spring and summer are weaker than those in autumn and winter and the correlation between PM 2.5 and PM 10 in the northwest is stronger than that in the southeast in each season.