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 In this study,a risk prediction model for Alzheimer's disease(AD)was constructed based on Alzheimer's disease neuroimaging initiative(ADNI)database to provide a theoretical basis for a better understanding of AD,as well as to provide a reference basis for the rational allocation of health resources and the development of preventive and control strategies.Methods The ADNIMERGE,UPENNBIOMK,ADNINIGHTINGALELONG and DXSUM datasets from the ADNI database were downloaded and random forest algorithm was used for the missing values imputation.Variable screening was performed by LASSO regression.Borderline-1 SMOTE resampling was used to adjusted for intergroup balance,and the variables were incorporated into base classifiers including XGBoost,random forest,bagging,AdaBoost,and support vector machine.Enhanced integrated classifiers were then constructed based on voting and Stacking strategies.Youden index,area under curve(AUC),F-score,G-mean,accuracy,Matthews correlation coefficient(MCC)and Kappa on the validation set were used to evaluate and compare the model efficacy.Results The performance of the classifiers based on balanced data were improved for both base and enhanced integrated classifiers.The performance of stacking and voting enhanced integrated classifiers constructed based on advantageous base classifiers have better performance compared to the base classifiers.After data balancing,the XGBoost performed better in the base classifiers(AUC:0.9090,accuracy:0.9091)and voting algorithm performs better in enhanced integrated classifiers(AUC:0.9178,accuracy:0.9179).Conclusion After Borderline-1 SMOTE resampling adjusted,the performance of both base classifiers and the enhanced integrated classifiers were all improved.For balanced data,XGBoost classifier and the voting enhanced integrated classifier can effectively assist in clinical prediction of Alzheimer's disease progression.

Objective In this study,a risk prediction model for Alzheimer's disease(AD)was constructed based on Alzheimer's disease neuroimaging initiative(ADNI)database to provide a theoretical basis for a better understanding of AD,as well as to provide a reference basis for the rational allocation of health resources and the development of preventive and control strategies.Methods The ADNIMERGE,UPENNBIOMK,ADNINIGHTINGALELONG and DXSUM datasets from the ADNI database were downloaded and random forest algorithm was used for the missing values imputation.Variable screening was performed by LASSO regression.Borderline-1 SMOTE resampling was used to adjusted for intergroup balance,and the variables were incorporated into base classifiers including XGBoost,random forest,bagging,AdaBoost,and support vector machine.Enhanced integrated classifiers were then constructed based on voting and Stacking strategies.Youden index,area under curve(AUC),F-score,G-mean,accuracy,Matthews correlation coefficient(MCC)and Kappa on the validation set were used to evaluate and compare the model efficacy.Results The performance of the classifiers based on balanced data were improved for both base and enhanced integrated classifiers.The performance of stacking and voting enhanced integrated classifiers constructed based on advantageous base classifiers have better performance compared to the base classifiers.After data balancing,the XGBoost performed better in the base classifiers(AUC:0.9090,accuracy:0.9091)and voting algorithm performs better in enhanced integrated classifiers(AUC:0.9178,accuracy:0.9179).Conclusion After Borderline-1 SMOTE resampling adjusted,the performance of both base classifiers and the enhanced integrated classifiers were all improved.For balanced data,XGBoost classifier and the voting enhanced integrated classifier can effectively assist in clinical prediction of Alzheimer's disease progression.

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 investigate the associations of onset age, diabetes duration, and glycated hemoglobin (HbA1c) levels with ischemic stroke risk in type 2 diabetes patients.Methods:The participants were from Comprehensive Research on the Prevention and Control of the Diabetes in Jiangsu Province. The study used data from baseline survey from December 2013 to January 2014 and follow-up until December 31, 2021. After excluding the participants who had been diagnosed with stroke at baseline survey and those with incomplete information on onset age, diabetes duration, and HbA1c level, a total of 17 576 type 2 diabetes patients were included. Cox proportional hazard model was used to calculate the hazard ratio ( HR) and 95% CI of onset age, diabetes duration, and HbA1c level for ischemic stroke. Results:During the median follow-up time of 8.02 years, 2 622 ischemic stroke cases were registered. Multivariate Cox proportional risk regression model showed that a 5-year increase in type 2 diabetes onset age was significantly associated with a 5% decreased risk for ischemic stroke ( HR=0.95, 95% CI: 0.92-0.99). A 5-year increase in diabetes duration was associated with a 5% increased risk for ischemic stroke ( HR=1.05, 95% CI: 1.02-1.10). Higher HbA1c (per 1 standard deviation increase: HR=1.17, 95% CI: 1.13-1.21) was associated with an increased risk for ischemic stroke. Conclusion:The earlier onset age of diabetes, longer diabetes duration, and high levels of HbA1c are associated with an increased risk for ischemic stroke in type 2 diabetes patients.

Hypoxia is associated with the occurrence and development of many diseases in clinical settings.Cell hypoxia not only serves as a vital marker for disease advancement,but also plays a pivotal role in exacerbating the disease process,and improving tissue hypoxia may thus provide new strategies for the treatment of related diseases.Further investigation of these diseases at the cellular and molecular levels requires the establishment of a cellular hypoxia model.Current extensively employed hypoxic cell models can be categorized primarily into three types:chemical hypoxia,physical hypoxia,and glucose deprivation hypoxia models.This article reviews the various types of hypoxic cell models and scrutinizes their applications and limitations in disease research.

Hypoxia is associated with the occurrence and development of many diseases in clinical settings.Cell hypoxia not only serves as a vital marker for disease advancement,but also plays a pivotal role in exacerbating the disease process,and improving tissue hypoxia may thus provide new strategies for the treatment of related diseases.Further investigation of these diseases at the cellular and molecular levels requires the establishment of a cellular hypoxia model.Current extensively employed hypoxic cell models can be categorized primarily into three types:chemical hypoxia,physical hypoxia,and glucose deprivation hypoxia models.This article reviews the various types of hypoxic cell models and scrutinizes their applications and limitations in disease research.

Objective:To investigate the clinical characteristics of asymptomatic carriers with 2019 novel coronavirus (2019-nCoV), and to provide clinical guidance for the management of asymptomatic infection with 2019-nCoV.Methods:The clinical data of 663 patients with confirmed coronavirus disease 2019 (COVID-19) admitted to Renmin Hospital of Wuhan University from January 11 to February 6, 2020 were collected. Patients were divided into asymptomatic group (21 cases) and symptomatic group (642 cases) according to the diagnostic criteria. General conditions, clinical classification, death, chest computed tomograph (CT) and laboratory results of patients were retrospectively collected. Mann-Whitney U test, chi-square test and Fisher exact test were used for statistical analysis. Results:All 663 patients were positive for 2019-nCoV nucleic acid tests. The age of patients in the asymptomatic group were significantly younger than those in symptomatic group (35.0 (31.5, 58.0) years old vs 58.5 (45.0, 69.0) years old, U=4 234.500, P=0.002). The proportion of patients <30 years old in the two groups was significantly different (19.0%(4/21) vs 6.1%(39/642), Fisher exact test, P=0.047). There were 15 women (71.4%) in the asymptomatic group and 327 women (50.9%) in the symptomatic group, while the difference of gender distributions was not statistically significant ( χ2=3.420, P=0.064). In addition, among patients with asymptomatic infection, the proportions of mild/ordinary, severe and critical patients were 10 cases (47.6%), 10 cases (47.6%), and one case (4.8%), respectively, which were not significantly different from those in symptomatic group (244 cases (38.0%), 305 cases (47.5%) and 93 cases (14.5%), respectively, χ2=1.847, P=0.397). As of February 9, one(4.8%) mild/ordinary patient in the asymptomatic group died who had malignant tumor. Twenty-four (3.7%) patients in the symptomatic group died including two mild/ordinary and 22 critical patients. There was no significant difference in mortality between the two groups(Fisher exact test, P=0.560). CT examination was performed on 594 patients, and 591 cases (99.5%) showed unilateral or bilateral pneumonia, and three cases (0.5%) showed normal. Conclusions:Patients with asymptomatic infection with 2019-nCoV are younger than symptomatic patients, and there are more patients under 30 years old in the asymptomatic group. The absence of clinical symptoms is not significantly associated with clinical classifications and mortality in COVID-19 patients.

Objective:To retrospectively analyze the clinical characteristics of patients with coronavirus disease 2019 (COVID-19) accompanied with diarrhea.Methods:From January 11 to February 6 in 2020, the clinical data of 663 patients diagnosed with COVID-19 admitted to Renmin Hospital of Wuhan University were collected and divided into diarrhea group and non-diarrhea group according to whether they had diarrhea or not. The differences in baseline characteristics, basic disease history, clinical manifestations, chest computed tomography (CT), laboratory findings, disease severity and mortality between the two groups were compared. Chi-square test and Fisher exact test were used for statistical analysis.Results:Among 663 COVID-19 patients, 70 (10.6%) patients accompanied with diarrhea. The proportion of fatigue and increased lactate dehydrogenase (LDH) levels of diarrhea group were higher than those of non-diarrhea group (58.6%, 41/70 vs. 28.2%, 167/593; and 64.2%, 43/67 vs. 50.4%, 277/550), and the differences were statistically significant ( χ2=26.891 and 4.566, both P<0.05). There was no statistically significant difference in the proportion of pneumonia in chest CT between diarrhea group and non-diarrhea group (100.0%, 62/62 vs. 99.4%, 529/532) ( P>0.05). There were no statistically significant differences in the proportions of mild and normal type, severe type and critical type between diarrhea group and non-diarrhea group (35.7%, 25/70 vs. 38.6%, 229/593; 50.0%, 35/70 vs. 47.2%, 280/593; and 14.3%, 10/70 vs. 14.2%, 84/593, respectively) (all P>0.05). There were no statistically significant differences in the mortality of mild and normal type, severe type and critical type between diarrhea group and non-diarrhea group (0 vs. 0.5%, 3/593; 0 vs. 0 and 1.4%, 1/70 vs. 3.5%, 21/593) (all P>0.05). Conclusions:Patients with COVID-19 accompanied with diarrhea are more likely to have fatigue and increased LDH level. Diarrhea is not significantly correlated with the disease severity of patients with COVID-19.

Poor experience of teacher-student interaction and low user loyalty exist in MOOC (massive open online courses).Therefore,the campus open online courses (COOC),a network teaching platform,was developed to integrate traditional classroom teaching and network teaching and to build an online-offline curriculum system according to professional training scheme of school.The online-offline teaching design and learning evaluation in COOC platform was also developed.The running data of COOC platform showed that the online-offline learning evaluation model has guiding impact on students' learning attitude because it can record the students' learning process and learning effect,which can enhance students' active participation in self-directed learning.The learning evaluation model in COOC is objective and scientific,which is helpful to improve the quality of teaching and learning.