Objective:To analyze the association between the triglyceride-glucose(TyG)index and the risk of nephrolithiasis across various demographic and clinical subgroups,aiming to enhance early di-agnosis and treatment of nephrolithiasis and promote personalized care in diverse populations.Methods:This cross-sectional study analyzed the medical records of 84 968 adults,stratified into three categories(low,middle,high)according to their TyG index scores.To evaluate the association between the TyG index and nephrolithiasis risk,multivariable Logistic regression models were employed,adjusting for po-tential confounders.Additionally,piecewise linear regression models were used to investigate the non-linear dynamics of the TyG index's relationship with nephrolithiasis risk.Subgroup analyses were per-formed to explore variations in the effects of the TyG index across different demographic and clinical populations.Results:Increasing TyG index was associated with a higher risk of nephrolithiasis,rising from 4.36％in the low group to 8.96％in the high group(P＜0.001).In adjusted models,males in the middle and high TyG index categories demonstrated significantly elevated risks of nephrolithiasis,with odds ratios of 1.18(95％CI:1.07-1.31,P=0.002)and 1.29(95％CI:1.15-1.45,P＜0.001),respectively.Conversely,in females,the association was not statistically significant post-adjustment(OR=0.98,95％CI:0.82-1.16,P=0.778).Among males,for each unit increment in the TyG index be-low the critical threshold of 8.98,there was a notable 40％escalation in the risk of developing nephroli-thiasis(OR=1.40,95％CI:1.24-1.58,P＜0.001).Surpassing this threshold,the TyG index no longer conferred a significant increase in risk(OR=0.91,95％CI:0.78-1.06,P=0.24).Subgroup analyses indicated that this association remained stable regardless of age,BMI,or hypertension status.Conclusion:The TyG index is positively associated with the risk of nephrolithiasis in males,demonstra-ting a nonlinear dose-response relationship that becomes especially pronounced at certain index levels.This biomarker could potentially serve as a valuable clinical tool for identifying males who are at a high risk of developing nephrolithiasis,thereby enabling targeted preventive strategies.Further research is urgently needed to explore the underlying mechanisms and to verify the applicability of these results across different populations.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.

BACKGROUND:N6-methyladenosine(m6A)is a hot research topic in the mechanism of pathological cardiac remodeling and plays an important role in the development of cardiovascular diseases. OBJECTIVE:To summarize the possible mechanism by which m6A modification in non-coding RNAs regulates the main processes of pathological cardiac remodeling,such as pathological cardiac hypertrophy,cardiomyocyte death,myocardial fibrosis and vascular remodeling. METHODS:"m6A,non-coding RNA,pathological cardiac hypertrophy,cardiomyocyte apoptosis,cardiomyocyte pyroptosis,cardiomyocyte ferroptosis,myocardial fibrosis,vascular remodeling"were used as search terms in Chinese and English.Relevant literature from CNKI,PubMed and Web of Science databases published from January 1974 to April 2023 was retrieved,and finally 86 eligible articles were reviewed. RESULTS AND CONCLUSION:m6A modification is a highly dynamic and reversible modification.Pathological cardiac remodeling mainly involves pathological cardiac hypertrophy,cardiomyocyte apoptosis,cardiomyocyte pyroptosis,cardiomyocyte ferroptosis,myocardial fibrosis and vascular remodeling.m6A-related enzymes can regulate pathological cardiac remodeling processes through various non-coding RNAs and different signaling pathways,which can be used as a new potential intervention for cardiovascular diseases.In pathological cardiac remodeling,research on the regulatory relationship between m6A modification and non-coding RNAs is still in its infancy.With the development of epigenetics,m6A modification in non-coding RNAs is expected to have a new development in the regulation of pathological cardiac remodeling.

Objective:To measure the retinal structural and functional parameters of adult Macaca fascicularis, and explore the similarity of the retinal structural and functional parameters between non-human primates and normal human retinas.Methods:Six eyes of 3 5-year-old adult Macaca fascicularis were examined by in vivo detection including color fundus photography, retinal optical coherence tomography (OCT) and electroretinogram (ERG) to determine the thickness of the inner/outer retina at the fovea and 1 000/2 000 μm away from the nasal, temporal, superior and inferior regions of the fovea, the thickness of the retinal nerve fiber layer (RNFL), the area of optic disc, the area of optic cup, the area ratio of cup to disc and the biological parameters of flash ERG.Differences in the above parameters between left and right eyes were analyzed.The similarity of parameters between Macaca fascicularis and human was compared with reference to published literature.The use and care of animals complied with the Regulation on the Management of Experimental Animals.The study protocol was approved by the Institutional Animal Care and Use Committee of Hubei Topgene Biotechnology (NO.IACUC-2019-012). Results:The foveal thickness, optic disc area, cup-disc area ratio, and average RNFL thickness in normal adult Macaca fascicularis were (252.31±4.79)μm, (1.89±0.05)mm 2, 0.14±0.01, and (103.53±0.58)μm, respectively.The b-wave amplitude of dark-adapted 0.01 ERG was (66.75±7.29)μV.The a- and b-wave amplitudes of dark-adapted 3.0 ERG response were (57.15±15.01) and (122.10±25.51)μV, respectively.The a- and b-wave amplitudes, the amplitude of oscillation potentials, and the latency of dark-adapted 10.0 ERG response were (72.98±20.14)μV, (131.67±13.78)μV, (49.98±10.08)μV, and (30.02±5.76)ms, respectively.The a- and b-wave amplitudes of light-adapted 3.0 ERG were (9.16±2.75) and (40.43±5.57)μV, respectively.The latency and the amplitude of the light-adapted 30 Hz flicker was (26.61±1.19)ms and (24.72±5.10)μV, respectively.There was no significant difference in the parameters between left and right eyes (all at P>0.05). The retinal thickness in central fovea, mean RNFL thickness, waveform and amplitude of ERG of Macaca fascicularis were similar to normal human. Conclusions:The structure and function of the retina of adult Macaca fascicularis are similar to those of normal humans.As a laboratory animal for preclinical drug research, in vivo studies of Macaca fascicularis can refer to normal human retinal parameters.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.