Objective To develop and validate a prediction model for severe disability or death(SDD)in acute ischemic stroke(AIS)patients who underwent endovascular treatment(EVT).Methods Based on the dataset of ANGEL-ACT study who received EVT for AIS between november 2017 and march 2019,a retrospective analysis was performed on 1 677 patients,including 1 111 males and 566 females,aged ≥ 18 years.Patients were divided into two groups according to whether SDD occurred(mRS 5-6 scores 90 days after surgery):SDD group(n=478)and non-SDD group(n=1 199).Risk factors that might influence SDD after EVT in AIS patients were screened and analyzed by multifactorial analysis,LAS-SO regression,and RF-RFE methods.A nomogram was developed after evaluating the model performance and the execution of internal validation.Results SDD occurred in 380(28.1％)patients in the develop-ment cohort and 98(30.2％)patients in the validation cohort.Combining the three variable screening meth-ods,10 risk factors were selected for inclusion in the final model:age,NIHSS score,whether successful re-canalization,glucose level,hemoglobin,hematocrit,onset to puncture time,systolic blood pressure,AS-PECT score,and whether have treatment-related serious adverse events.A two-stage model means that model 1 contains pre-treatment variables(7 in total)and model 2 contains pre-treatment and post-treatment variables(10 in total).The area under the curve(AUC)of model 1 in the development cohort was 0.705(95％CI 0.674-0.736)and 0.731(95％CI 0.701-0.760)in model 2.Both models had good calibration with aslope of 1.000,and the decision curve analysis showed good clinical applicability.The results of the validation cohort were similar to those of the development cohort.Conclusion Age,admission NIHSS score,whether successful recanalization,admission glucose level,hemoglobin content,erythrocyte pressure volume,onset to puncture time,admission systolic blood pressure,ASPECT score,and whether have treat-ment-related serious adverse events are risk factors for SDD in patients with acute ischemic stroke.The two prediction models based on the above factors were used before and after endovascular treatment to predict SDD occurrence better.

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
Humans ; Proteome/metabolism* ; Proteomics ; Prospective Studies ; Obesity/genetics* ; Genomics ; Genome-Wide Association Study

Objective To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis. Methods The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model. Results A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ² = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ² = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = −0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05). Conclusions The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.