Objective To explore the quantitative electroencephalography(qEEG)characteristics of the prefrontal cortex in patients with mild cognitive impairment(MCI)during digital screening tasks for MCI screening.Methods A total of 592 MCI patients(MCI group)and 317 normal cognitively elderly individuals(control group)were recruited from 40 communities in Nanjing,Jiangsu Province,from July to August,2024.All participants were as-sessed using Montreal Cognitive Assessment-Beijing Version(MoCA-BJ).Prefrontal EEG data were collected using a portable EEG device,and power spectral analysis was performed via Fast Fourier Transform.An XG-Boost algorithm was employed to construct an MCI identification model based on qEEG power features,and the model's performance was evaluated using receiver operating characteristic(ROC)curve.Results Compared with the control group,prefrontal δ,α,and β band power increased during screening tasks in MCI group(P<0.05);δ power was negatively correlated with MoCA-BJ total scores,and visuospatial/executive func-tion,attention and delayed recall scores(r=-0.269,-0.169,-0.133,-0.171,P<0.001);α power was negative-ly correlated with MoCA-BJ total scores,attention and delayed recall scores(r=-0.113,-0.075,-0.091,P<0.05).The XGBoost model based on δ and α power was excellent in MCI identification,with an area under the curve of 0.91,accuracy of 0.81,precision of 0.89,F1 score of 0.84,recall of 0.80,and specificity of 0.81.Conclusion MCI patients exhibit increased power in the prefrontal δ and α frequency bands during digital screening tasks,which is associated with cognitive decline.An XGBoost model based on qEEG power features can enable early prediction of MCI.

Objective To explore the quantitative electroencephalography(qEEG)characteristics of the prefrontal cortex in patients with mild cognitive impairment(MCI)during digital screening tasks for MCI screening.Methods A total of 592 MCI patients(MCI group)and 317 normal cognitively elderly individuals(control group)were recruited from 40 communities in Nanjing,Jiangsu Province,from July to August,2024.All participants were as-sessed using Montreal Cognitive Assessment-Beijing Version(MoCA-BJ).Prefrontal EEG data were collected using a portable EEG device,and power spectral analysis was performed via Fast Fourier Transform.An XG-Boost algorithm was employed to construct an MCI identification model based on qEEG power features,and the model's performance was evaluated using receiver operating characteristic(ROC)curve.Results Compared with the control group,prefrontal δ,α,and β band power increased during screening tasks in MCI group(P<0.05);δ power was negatively correlated with MoCA-BJ total scores,and visuospatial/executive func-tion,attention and delayed recall scores(r=-0.269,-0.169,-0.133,-0.171,P<0.001);α power was negative-ly correlated with MoCA-BJ total scores,attention and delayed recall scores(r=-0.113,-0.075,-0.091,P<0.05).The XGBoost model based on δ and α power was excellent in MCI identification,with an area under the curve of 0.91,accuracy of 0.81,precision of 0.89,F1 score of 0.84,recall of 0.80,and specificity of 0.81.Conclusion MCI patients exhibit increased power in the prefrontal δ and α frequency bands during digital screening tasks,which is associated with cognitive decline.An XGBoost model based on qEEG power features can enable early prediction of MCI.

Objective Although principal components analysis profiles greatly facilitate the visualization and interpretation of the multivariate data,the quantitative concepts in both scores plot and loading plot are rather obscure.This article introduced three profiles that assisted the better understanding of metabolomic data.Methods The discriminatory profile,heat map,and statistic profile were developed to visualize the multivariate data obtained from high-throughput GC-TOF-MS analysis.Results The discriminatory profile and heat map obviously showed the discriminatory metabolites between the two groups,while the statistic profile showed the potential markers of statistic significance.Conclusion The three types of profiles greatly facilitate our understanding of the metabolomic data and the identification of the potential markers.

Objective To improve the diagnostic rate of mammary ductal disease by galactography and mammoscopy.Methods 100 cases of the galactographic and mommoscopy materials were retrospectively analyzed.Results Tumorous diseases including intraductal papilloma,intraductal papillary carcinoma and ductal carcinoma,they accounted for 14%,intraductal papilloma and intraductal papillary carcinoma were the most in this group(12%).Non-tumorous diseases including mammary ductal ectasia with chronicmastitis,plasma cell mastitis,mammary cyst,lobular hyperplasia and cystic hyperplasia,they accounted for 71%,mammary ductal ectasia was the most in this group(42%).Conclusion The galactography and mammoscopy are very valuable in the diagnosis and differentiating diagnosis of mammary ductal diseases.