Objective:To explore the causal association between insulin-like growth factor-1(IGF-1)and colorectal cancer(CRC)based on two sample Mendelian randomization(MR)analysis.Methods:A bidirectional two sample MR analysis was conducted based on publicly aggregated data from the IEU OpenGWAS project.The inverse variance weighted(IVW)method was used as the main analysis model to assess the causal relationship between IGF-1 and CRC.Additional analyses were performed using weighted median(WM),MR-Egger regression,weighted mode estimator(WME),and simple mode(SM)methods.Sensitivity analysis was performed to assess the robustness of the results.Results:A total of 386 single nucleotide polymorphisms(SNPs)were selected as instrumental variables(IVs)with IGF-1 as the exposure factor.The MR analysis results revealed a positive causal association between IGF-1 and the risk of CRC[odds ratio(OR)=1.178,95％confidence interval(CI):1.092-1.272)](P＜0.001),and the association remained significant after adjusting for height[OR(95％CI)=1.214(1.111,1.327)](P＜0.001).Cochran's Q-test showed heterogeneity among the IVs(P＜0.05),while the horizontal pleiotropy of IV was not detected by the MR-Egger regression(P＞0.05).The leave-one-out analysis showed that the MR results were robust.Reverse MR analysis indicated no reverse causal relationship between IGF-1 and CRC[OR(95％CI):1.017(0.997,1.037)](P=0.103).Conclusion:There is a causal relationship between IGF-1 level and CRC,and elevated IGF-1 level could be a risk factor for CRC.

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Objective:To investigate the diagnosis of dual-energy X-ray absorptiometry(DXA)for osteoporosis(OP)of postmenopausal patients with rheumatoid arthritis(RA)in Qinghai region and the risk factors of them.Methods:A total of 200 postmenopausal female RA patients who admitted to Qinghai Hospital of Traditional Chinese Medicine from May 2022 to April 2023 were selected.All patients were tested for bone mineral density(BMD)after admission,and lumbar spines L1-L4,whole lumbar,large trochanter,Ward's triangle area,whole body and whole forearm were measured by DXA.According to the results of BMD test,patients whose BMD T values of all body parts-2.5 SD were less or equal to-2.5 were included in the OP group(121 cases),and patients whose BMD T value of all body parts were larger than-2.5 SD were included in the non-OP group(79 cases).The BMD T value of different body parts between two groups of RA patients were compared and analyzed.The area under curve(AUC)of receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficiency of BMD T value for OP.The logistic regression method was adopted to analyze the risk factors that postmenopausal RA patients of Qinghai region occurred OP.Results:The BMD T values of L1,L2,L3,L4,whole lumbar,large trochanter,Ward's triangular area,whole body and whole forearm of OP group were obviously lower than those of the non-OP group.In analysis of ROC curve,the sensitivities of BMD T values of L1,L2,L3,L4,whole lumbar,large trochanter,Ward's triangle area,whole body and forearm were respectively 96.20%,95.22%,90.16%,96.03%,92.01%,89.36%,99.26%,90.02% and 96.03% in diagnosing OP,and the specificities of them were respectively 81.00%,82.19%,85.22%,83.06%,83.06%,90.22%,80.06%,86.23%,83.09%,and the AUC values of them were respectively 0.908,0.905,0.896,0.906,0.903,0.879,0.918,0.901 and 0.906.The results of the logistic-regression analysis showed that advanced age,long disease course,rheumatic activity scores of 28 joints,erythrocyte sedimentation rate and Calcium supplementation were the risk factors of occurring OP in postmenopausal RA patients in Qinghai region.Conclusion:The DXA method that detects BMD of RA patients who occur OP can be used as gold standard to assess OP,and there are many risk factors that affect the occurrence of OP in postmenopausal RA patients of Qinghai region.The clinical work should combine with relative factors to formulate reasonable measure so as to reduce the incidence of OP.

Objective:To explore the mechanism of ubiquitin-specific protease 11(USP11)affecting the proliferation and invasion of oral squamous cell carcinoma(OSCC)cells by regulating IGF2BP3 expression.Methods:USP11 expression in OSCC tissues and adja-cent tissues from OSCC patients(n=50)was detected by immunohistochemistry and western blot,and USP11 expression in normal hu-man oral keratinocyte(HOK)cell line and human OSCC cell lines SCC-25 and CAL-27 was detected by western blot.SCC-25 and CAL-27 cells were transfected with siRNA USP11(si-USP11)or siRNA negative control(si-NC).Western blot was performed to de-tect the silencing efficiency of USP11.CCK-8,wound healing assay and Transwell assay were carried out to evaluate the effects of USP11 silencing on cell proliferation,migration and invasion.Western blot was employed to detect IGF2BP3 expression after the knockdown of USP11.Nude mice were inoculated with SCC-25 cells to construct the transplanted tumor model,and the inhibitory effect of USP11 knock-down on SCC-25 cell tumorigenicity was investigated.Results:The USP11 protein level in carcinoma tissues of OSCC patients was significantly higher than in the adjacent tissues,USP11 protein expression was significantly higher in SCC-25 and CAL-27 cells than in HOK cells.The knockdown of USP11 markedly reduced the proliferation,migration and invasion of SCC-25 and CAL-27 cells,and down-regulated the expression of IGF2BP3 cells.Compared with the USP11 silencing group,the proliferation,migration and invasion of SCC-25 and CAL-27 cells were significantly increased in the simultaneous knockdown of USP11 and overexpression of IGF2BP3 cells.Compared with the USP11 overexpression group,the proliferation,migration and invasion of SCC-25 and CAL-27 cells were decreased in the simultaneous IGF2BP3 knockdown and USP11 overexpression cells.Tumorigenicity experiments in nude mice showed that the tumor volume and weight were significantly declined by USP11 knockdown.Conclusion:USP11 is highly expressed in OSCC tissues,which may promote the proliferation,migration and invasion of OSCC cells through up-regulation of IGF2BP3 expression.

Objective:To screen the main characteristic variables affecting the incidence of cardiovascular and cerebrovascular diseases,and to construct the Bayesian network model of cardiovascular and cerebrovascular disease incidence risk based on the top 10 characteristic variables,and to provide the reference for predicting the risk of cardiovascular and cerebrovascular disease incidence.Methods:From the UK Biobank Database,315 896 participants and related variables were included.The feature selection was performed by categorical boosting(CatBoost)algorithm,and the participants were randomly divided into training set and test set in the ratio of 7∶3.A Bayesian network model was constructed based on the max-min hill-climbing(MMHC)algorithm.Results:The prevalence of cardiovascular and cerebrovascular diseases in this study was 28.8％.The top 10 variables selected by the CatBoost algorithm were age,body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),total cholesterol(TC),the triglyceride-glucose(TyG)index,family history,apolipoprotein A/B ratio,high-density lipoprotein cholesterol(HDL-C),smoking status,and gender.The area under the receiver operating characteristic(ROC)curve(AUC)for the CatBoost training set model was 0.770,and the model accuracy was 0.764;the AUC of validation set model was 0.759 and the model accuracy was 0.763.The clinical efficacy analysis results showed that the threshold range for the training set was 0.06-0.85 and the threshold range for the validation set was 0.09-0.81.The Bayesian network model analysis results indicated that age,gender,smoking status,family history,BMI,and apolipoprotein A/B ratio were directly related to the incidence of cardiovascular and cerebrovascular diseases and they were the significant risk factors.TyG index,HDL-C,LDL-C,and TC indirectly affect the risk of cardiovascular and cerebrovascular diseases through their impact on BMI and apolipoprotein A/B ratio.Conclusion:Controlling BMI,apolipoprotein A/B ratio,and smoking behavior can reduce the incidence risk of cardiovascular and cerebrovascular diseases.The Bayesian network model can be used to predict the risk of cardiovascular and cerebrovascular disease incidence.

Objective:To discuss the clinical efficacy of recombinant human growth hormone(rhGH)in the treatment of the pediatric patients with growth hormone deficiency(GHD)and idiopathic short stature(ISS),and to clarify its clinical application value in the pediatric patients with short stature of different etiologies.Methods:The clinical data of 132 children with short stature who treated with rhGH from January 2018 to January 2023 were collected.They were divided into GHD group(n=70)and ISS group(n=62)based on different etiologies.The bone age,target height(TH),body mass index(BMI),height standard deviation score(HtSDS),changes in height standard deviation scores(ΔHtSDS)before treatment and 6 months after treatment,and growth velocity(GV)of the pediatric patients were calculated.Propensity score matching(PSM)and inverse probability of treatment weighting(IPTW)were used to balance the confounding factors between the pediatric patients in two groups and the efficacy and safety of the pediatric patients in two groups were evaluated.Results:There were significant differences in whether children were full-term,bone age,bone age maturity,and TH of the pediatric patients between two groups(P<0.05).Compared with before treatment,the height and HtSDS of the pediatric patients in both GHD and ISS groups were significantly increased after treated for 6 months(P<0.05).Before matched by PSM,there were significant differences in full-term,bone age,bone age maturity,and TH of the pediatric patients between two groups(P<0.05).After matched by PSM,there were no significant differences in gender,region,term birth status,mode of delivery,feeding method,age,bone age,height,BMI,TH,and pretreatment HtSDS of the pediatric patients between two groups(P>0.05);the standardized mean difference(SMD)differences of covariates except for region were<0.2.After weighted by IPTW,there were no significant differences in gender,region,term birth status,mode of delivery,feeding method,age,bone age,height,BMI,TH,and pretreatment HtSDS of the pediatric patients between two groups(P>0.05);all SMD of covariates except for term birth status were<0.2.Before balancing covariates,after meatched by PSM matching,and after weighted by IPTW weighting compared with GHD group,the GV and ΔHtSDS of the pediatric patients in ISS group were slightly increased,but the difference was not significant(P>0.05).In terms of adverse reactions,2 cases(2.68%)of fasting hyperglycemia and 7 cases(10.00%)of hypothyroidism occurred in GHD group;3 cases(4.84%)of fasting hyperglycemia and 2 cases(3.23%)of hypothyroidism occurred in ISS group.Conclusion:rhGH can promote the height increase in the patients with GHD and ISS,and there is no significant difference in the height-increasing efficacy between GHD and ISS children.The incidence of adverse reactions is relatively low during treatment,indicating good overall safety.

Objective:Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease.However,we still don't fully understand how certain immune-related genes contribute to the disease's development and progression.This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis(WGCNA)and machine learning. Methods:This study downloaded the gene chip data from the Gene Expression Omnibus(GEO)database,and used WGCNA to screen out important gene modules related to Parkinson's disease.Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)were used to analyze the the functions of immune-related genes and signaling pathways involved.Immune cell infiltration analysis was performed using the CIBERSORT package of R language.Using bioinformatics method and 3 machine learning methods[least absolute shrinkage and selection operator(LASSO)regression,random forest(RF),and support vector machine(SVM)],the immune-related genes of Parkinson's disease were further screened.A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes(hub)gene.The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. Results:A total of 218 immune genes related to Parkinson's disease were identified,including 45 upregulated genes and 50 downregulated genes.Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways.The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells,NK cells,CD8+ T cells,and B cells were higher in the samples of Parkinson's disease patients,while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients.ANK1 was screened out as the hub gene.The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction,iron homeostasis regulation,and immune system activation. Conclusion:This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods,suggesting its potential as a candidate therapeutic target for Parkinson's disease.

Objective:To study the influence of the severity of diabetic retinopathy (DR) on the visual function of patients with type 2 diabetes, to provide scientific basis for the early prevention and control of DR.Methods:This study was designed as a cross-sectional study, recruiting already-diagnosed type 2 diabetes patients in four community health service centers in Guizhou Province between February and September 2022. Employing the Chinese version of the Visual Function Index-14 (VF-14), assess the participants′ near vision, visual adaptation, subjective visual perception, and stereo vision, with higher scores indicating poorer visual function. Categorize the severity of each eye′s damage into no diabetic retinopathy (DR), mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, and proliferative diabetic retinopathy (PDR), and use a 5-level DR grading system to evaluate the overall severity of diabetic retinopathy in both eyes. Employing linear regression analysis to investigate the linear relationship between DR and visual function index. Local weighted regression evaluates the nonlinear relationship between the DR composite score and the scores of visual function, with a steeper slope indicating poorer visual function for that level.Results:A total of 542 patients with type 2 diabetes were investigated, including 244 (45.02%) males, 298 (54.98%) females, and 162 (29.89%) patients with DR. After adjusting for confounders, compared with those without DR, patients with binocular DR Had overall scores ( β=0.136, P=0.003), near vision ( β=0.163, P<0.001), visual adaptation ( β=0.092, P=0.042), subjective vision ( β=0.120, P=0.009) and stereo vision ( β=0.094, P=0.044) were higher than those without DR. There were no differences in visual functions between DR And monocular DR. The local weighted regression curve showed that near vision (slope: 23.78) and overall score (slope: 58.37) increased sharply from mild to moderate NPDR in both eyes. Visual adaptation (slope: 5.37, 7.72), subjective vision (slope: 6.53, 7.93), stereovision (slope: 0.74, 0.91) increased slowly in mild to moderate NPDR in both eyes and in moderate to severe NPDR/PDR in both eyes. Conclusion:Binocular DR is associated with impaired visual function, but there is no difference between monocular DR And non-DR visual function. The early damage of DR To visual function is mainly manifested in near vision. In the prevention and control of DR, more attention should be paid to visual function, especially the change of near vision, and retinal damage should not be assessed solely by visual status.

Objective:To study the influence of the severity of diabetic retinopathy (DR) on the visual function of patients with type 2 diabetes, to provide scientific basis for the early prevention and control of DR.Methods:This study was designed as a cross-sectional study, recruiting already-diagnosed type 2 diabetes patients in four community health service centers in Guizhou Province between February and September 2022. Employing the Chinese version of the Visual Function Index-14 (VF-14), assess the participants′ near vision, visual adaptation, subjective visual perception, and stereo vision, with higher scores indicating poorer visual function. Categorize the severity of each eye′s damage into no diabetic retinopathy (DR), mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, and proliferative diabetic retinopathy (PDR), and use a 5-level DR grading system to evaluate the overall severity of diabetic retinopathy in both eyes. Employing linear regression analysis to investigate the linear relationship between DR and visual function index. Local weighted regression evaluates the nonlinear relationship between the DR composite score and the scores of visual function, with a steeper slope indicating poorer visual function for that level.Results:A total of 542 patients with type 2 diabetes were investigated, including 244 (45.02%) males, 298 (54.98%) females, and 162 (29.89%) patients with DR. After adjusting for confounders, compared with those without DR, patients with binocular DR Had overall scores ( β=0.136, P=0.003), near vision ( β=0.163, P<0.001), visual adaptation ( β=0.092, P=0.042), subjective vision ( β=0.120, P=0.009) and stereo vision ( β=0.094, P=0.044) were higher than those without DR. There were no differences in visual functions between DR And monocular DR. The local weighted regression curve showed that near vision (slope: 23.78) and overall score (slope: 58.37) increased sharply from mild to moderate NPDR in both eyes. Visual adaptation (slope: 5.37, 7.72), subjective vision (slope: 6.53, 7.93), stereovision (slope: 0.74, 0.91) increased slowly in mild to moderate NPDR in both eyes and in moderate to severe NPDR/PDR in both eyes. Conclusion:Binocular DR is associated with impaired visual function, but there is no difference between monocular DR And non-DR visual function. The early damage of DR To visual function is mainly manifested in near vision. In the prevention and control of DR, more attention should be paid to visual function, especially the change of near vision, and retinal damage should not be assessed solely by visual status.