Multiple sclerosis(MS)is a complex autoimmune disorder characterized by inflammatory demyelination in the central nervous system.Prominent symptoms include damage to myelin sheaths in the brain,optic nerve,and spinal cord,as well as axonal dysfunction;however,the exact causes and mechanisms of MS remain unclear.Genetic and environmental factors are thought to interact via autoimmune mechanisms,potentially triggering the disease.Recent studies suggest that abnormal activation of the NOD-like receptor thermal protein domain associated protein 3(NLRP3)inflammasome may play a critical role in the pathogenesis of MS.In this context,this review summarizes the molecular mechanisms underlying NLRP3 activation and its connection to MS,considering relevant literature from the past decade.The findings aim to provide insights into the progression of MS and to identify potential therapeutic strategies by elucidating the underlying mechanisms.

Multiple sclerosis(MS)is a complex autoimmune disorder characterized by inflammatory demyelination in the central nervous system.Prominent symptoms include damage to myelin sheaths in the brain,optic nerve,and spinal cord,as well as axonal dysfunction;however,the exact causes and mechanisms of MS remain unclear.Genetic and environmental factors are thought to interact via autoimmune mechanisms,potentially triggering the disease.Recent studies suggest that abnormal activation of the NOD-like receptor thermal protein domain associated protein 3(NLRP3)inflammasome may play a critical role in the pathogenesis of MS.In this context,this review summarizes the molecular mechanisms underlying NLRP3 activation and its connection to MS,considering relevant literature from the past decade.The findings aim to provide insights into the progression of MS and to identify potential therapeutic strategies by elucidating the underlying mechanisms.

Background::Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables.Methods::A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction.Results::The 110 children were divided into two groups according to change in height-for-age Z-score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796). Conclusions::A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.

Objective To explore the value of low-dose CT in the diagnosis of congenital heart disease(CHD)with tracheal stenosis (CTS)in infants.Methods Data of low-dose CT and echocardiography in 18 cases of CHD with CTS were analyzed retrospectively, and compared with surgical findings.Results 18 cases of CST were comfirmed by surgical operation.The diagnostic accuracy of low-dose CT was 100%,and there was no statistical difference in the assessment of the range and degree in tracheal stenosis(P=0.76>0.05, P=0.767>0.05).42 cardiac anomalies were comfirmed by operation.The diagnostic accuracies of low-dose CT and echocardiography were 88.1% and 90.5% respectively,which was no statistical difference(P=0.825>0.05).15 cases of CTS(15/18,83.33%)were caused by the compression of the vessels,in which there were 9 cases by pulmonary artery sling,3 cases by vascular ring,3 cases by double aortic arches.Conclusion Low-dose CT can accurately diagnose CHD with CTS and provide important information for clinical practice.

Objective To analysis and compare the proteomic differences between neural stem cells and motor neurons in embryonic spinal cord in rat and discover the key different proteins. Methods Separating the protein of cells by the 2-D fluorescence difference gel electrophoresis, and to analyse the differences of protein expression with DeCyder software, and to identify with high performance liquid chromatography-electrospray tandem mass spectrometry. Results About 1 300 protein spots from the cells were gained after gel analysis. Eighty-seven protein spots were selected for mass spectrometry analysis. Fourty-four differently expressed proteins (24 in neural stem cells and 20 in motor neurons) were identified by mass spectrometry analysis.Conclusion Differently expressed proteins between neural stem cells and motor neurons were identified and it is helpful to find the new targets in neural stem cells differentiation into motor neurons.