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 differences in prognosis between different surgical approaches in elderly patients with advanced ovarian cancer.Methods:Based on the Surveillance, Epidemiology and End Results (SEER) database, a cohort of elderly patients with advanced ovarian cancer from 2000 to 2020 was established. Through propensity score matching, 2 094 patients were selected from those who underwent two different surgical approaches to form a matched cohort (SEER database cohort), including 1 039 patients who received cytoreductive surgery and 1 055 patients who underwent local resection. Meanwhile, 148 elderly patients with advanced ovarian cancer who were treated at the First Affiliated Hospital of Shandong First Medical University from January 2012 to January 2024 were selected (hospital cohort), among whom 85 underwent cytoreductive surgery and 63 underwent local resection. The prognostic differences among patients who underwent cytoreductive surgery and local resection in two cohorts and stratified by the International Federation of Gynecology and Obstetrics (FIGO) staging were evaluated, respectively. The relationship between the causes of death and surgical approaches in elderly patients with advanced ovarian cancer was analyzed.Results:In the SEER database cohort, the median overall survival (OS) for patients who underwent cytoreductive surgery and local resection was 37 and 40 months, respectively, with 5-year OS rates of 31.47% and 33.74%, with no statistically significant difference ( χ2=0.78, P=0.378). After stratification by FIGO staging, the median OS for patients with stage ⅢB-ⅢC who underwent cytoreductive surgery ( n=998) and local resection ( n=962) was 38 and 40 months, respectively, with no statistically significant difference ( χ2=0.20, P=0.659). For patients with stage Ⅳ, the median OS for those who underwent cytoreductive surgery ( n=41) and local resection ( n=93) was 17 and 36 months, respectively, with a statistically significant difference ( χ2=9.37, P=0.002). Among 2 094 elderly patients with advanced ovarian cancer, 1 581 had clearly identified causes of death. In patients who underwent cytoreductive surgery, the proportions of deaths due to ovarian cancer and non-ovarian cancer were 94.52% (742/785) and 5.48% (43/785), respectively. In patients who underwent local resection, the proportions of deaths due to ovarian cancer and non-ovarian cancer were 91.46% (728/796) and 8.54% (68/796), respectively. There was a statistically significant difference in the distribution of causes of death between the two surgical approaches ( χ2=5.69, P=0.017). In the hospital cohort, the median OS for patients undergoing cytoreductive surgery and local resection was 39 and 51 months, respectively, with 5-year OS rates of 22.85% and 23.81%, with a statistically significant difference ( χ2=6.71, P=0.010). After stratification by FIGO staging, the median OS for patients with stage ⅢB-ⅢC undergoing cytoreductive surgery ( n=29) and local resection ( n=26) was 50 and 51 months, respectively, with no statistically significant difference ( χ2=0.15, P=0.699) ; for patients with stage Ⅳ undergoing cytoreductive surgery ( n=56) and local resection ( n=37), the median OS was 35 and 47 months, respectively, with a statistically significant difference ( χ2=6.55, P=0.011) . Conclusions:The survival outcomes of local resection in elderly patients with advanced ovarian cancer are not inferior to those of cytoreductive surgery. For FIGO stage Ⅳ patients, the survival period following local resection is superior to that of cytoreductive surgery.

Objective To explore the influencing factors of depression risk in elderly patients with hypertension and construct and validate a nomogram prediction model.Methods A total of 869 elderly patients with hypertension were selected from national survey database of the China Health and Retirement Longitudinal Study(CHARLS)in 2018.Multivariate Logistic regression analysis was used to identify the risk factors for depression in elderly patients with hypertension,and a nomogram prediction model was constructed.The accuracy and effectiveness of the model were validated by the Hosmer-Lemeshow(H-L)goodness-of-fit test,the area under the curve(AUC)of the receiver oper-ating characteristic(ROC)curve,and the calibration curve.Results The incidence of depression in elderly patients with hypertension was 47.18％.Factors influencing the risk of depression included rural residence(OR=2.191,P＜0.05),impaired basic activities of daily living(BADL)(OR=2.338,P＜0.05),impaired instrumental activitiesofdaily living(IADL)(OR=1.674,P＜0.05),poor life satisfaction(OR=7.348,P＜0.05),fair self-rated health(OR=0.441,P＜0.05),good self-rated health(OR=0.259,P＜0.05),and sleep duration of 6 to 9 hours(OR=0.510,P＜0.05).The AUC of the ROC curve was 0.795,the slope of the calibration curve was close to 1,and the H-L goodness-of-fit test yielded x2=5.074.The validation set showed an AUC of 0.703.Conclusion The prediction model established in this study has high accuracy and discriminative ability.Healthcare professionals can take effective preventive measures based on individual patient factors.

Objective:To investigate the causal association between testosterone and nonalcoholic fatty liver disease(NAFLD) in men and women using a two-sample Mendelian randomization(MR) approach.Methods:Genetic variation in testosterone(total testosterone, bioavailable testosterone) and sex hormone-binding globulin(SHBG) in females and males was used as an instrumental variable using the genome-wide association study(GWAS) pooled data, and the inverse variance weighting method was applied. Inverse variance weighted(IVW) was used as the main analytical method, along with six univariate MR methods based on other modeling assumptions to assess the causal relationship between testosterone(total testosterone, bioavailable testosterone) as well as SHBG and NAFLD in women and men. In addition, NAFLD data from Finnish Biobank(FinnGen) were applied to validate the results of the exploratory analysis. Further, sensitivity analyses were performed to assess the level of heterogeneity, genetic pleiotropy, and stability of the instrumental variables using Cochran′ s Q test, MR-Egger regression, and leave-one-out methods. Results:The results of exploratory analysis of IVW model showed that bioavailable testosterone and SHBG were causally associated with NAFLD in women, for each unit increase in bioavailable testosterone levels, the risk of developing non-alcoholic fatty liver disease(NAFLD) rose by 24%( OR=1.24, 95% CI 1.07-1.43, P=0.004); and with each unit decrease in women′s SHBG, the NAFLD risk increased by 31%( OR=0.69, 95% CI 0.57-0.83, P<0.001). However, testosterone(total testosterone, bioavailable testosterone) as well as SHBG in men and female total testosterone did not show a causal relationship with NAFLD. The results of the other six MR methods were generally consistent with the IVW method. The results of the external validation data provided further evidence of a causal relationship between female bioavailable testosterone and SHBG and NAFLD. Conclusion:Elevated levels of bioavailable testosterone along lower levels of SHBG may increase the risk of developing NAFLD in women.

Objective:To reveal the research hotspots in hospital supply chain management in China and explore how supply chain management can facilitate the high-quality development of public hospitals.Methods:Bibliometric analysis method was employed, retrieving the Chinese literature on hospital supply chain management from 2000 to 2022 from CNKI, WeiPu, and WanFang databases. Descriptive analysis and cluster analysis of high-frequency keywords were conducted.Results:Through cluster analysis of 34 high-frequency keywords in the 1 113 Chinese literature, it was found that current research on hospital supply chain management mainly focused on 7 research hotspots: big data information systems, procurement management, risk management, refined management, inventory management, supplier management, and traceability management.Conclusions:Future research could focus on construction of hospital supply chain performance evaluation systems, digital technology-driven supply chain transformation and upgrading, enhancing hospital supply chain resilience under risks, and sustainable supply chain management.

Objective:To further study the causes of acute occupational poisoning accidents, and to provide scientific basis and decision support for the prevention of accidents in advance.Methods:From September 2022 to May 2023, the literature was searched and 232 cases of acute occupational poisoning cases occurred from 2013 to 2022 were collected. The causal nodes of the accident were determined according to the expert score, and the interpretative structural model (ISM) was used to construct the correlation model between the causal nodes to obtain the hierarchical relationship between the factors. The influence of each causal node on the occurrence of acute occupational poisoning accidents was studied by using Bayesian network (BN), and the relationship and influence among the causal nodes were analyzed by Netica 5.18 software to establish the pre-prevention model of acute occupational poisoning accidents and identify the key causal factors.Results:A total of 23, 203, and 6 cases of significant, large, and medium acute occupational poisoning accidents were included, of which 179, 29, and 24 cases were asphyxiating gas, irritating gas, and mixed gas, respectively. ISM of acute occupational poisoning accidents divided the causal factors into a 7-layer and 3-level hierarchical structure model. Among them, operation conditions, protective measures, ventilation equipment, hidden trouble investigation, emergency management, illegal operation, equipment and facilities, and blind rescue were the direct causes of the occurrence and expansion of accidents. Warning devices, inspection situation, safety education situation, safety operation procedures, and technology in the production process were indirect influences that lead to the occurrence and expansion of accidents. Safety production responsibility system, enterprise supervision and management and government supervision were the deep-rooted influences. BN reasoning showed that the maximum probability causal chain of acute occupational poisoning accidents was as follows: safety production responsibility system→enterprise supervision and management→safety education and training→protective measures→accident occurrence. The key factors leading to the occurrence of acute occupational poisoning accidents were inadequate protective measures, equipment and facility failures, operational errors, ventilation equipment not being used properly and improper emergency management.Conclusion:In the prevention of acute occupational poisoning accidents, it is necessary to correctly use protective measures, test equipment and facilities before operation, operate according to regulations, ensure the normal use of ventilation equipment, and strengthen emergency management, so as to reduce the incidence of acute occupational poisoning accidents.

Objective:Evaluating the impact of various hypoglycemic events on the Health-Related Quality of Life(HRQoL)of diabetes patients to provide empirical data for health technology assessment.Methods:The data was collected from a tertiary hospital in Jinan,Shandong Province,China.Health utility was measured using the Time Trade-off(TTO)method,and disutility was calculated based on the formula.Subsequently,the intergroup differences among various hypoglycemic event groups were analyzed.Results:The hypoglycemic events'average utility was all lower than the average utility for diabetics without hypoglycemic events(baseline status).Severe hypoglycemic events(0.108)had higher disutility compared to non-severe hypoglycemic events(0.008).Furthermore,nocturnal severe/non-severe hypoglycemic events(0.118/0.008)exhibited higher average disutility than daytime severe/non-severe hypoglycemic events(0.098/0.007).Conclusion:Optimal diabetes management demands prioritizing the prevention of both severe hypoglycemic events and nocturnal hypoglycemic events.

In recent years,the physician-patient relationship has been tense,the physician-patient conflict has been escalating,and the number of irrational medical complaints has been constantly increasing,all of which have a negative impact on the public image of the hospital and the legal environment of medical treatment.From the perspective of behavioral economics,it analyzes the reasons for the occurrence of irrational medical complaints among outpatient patients by applying behavioral economics theories such as bounded rationality,social influence,herd effect,loss aversion,endowment effect,and mental accounting.It constructs a model of irrational complaint behaviors of outpatient patients based on behavioral economics theory,and tries to draw lessons from framing effect and nudge theory to propose countermeasures for hospitals to reduce irrational medical complaints and boost the complaint prevention management of medical institutions.