Addressing the enduring challenge of evaluating traditional Chinese medicines (TCMs), the integrated evidence chain-based effectiveness evaluation of TCMs (Eff-iEC) has emerged. This paper explored its capacity through a demonstration study that evaluated the effectiveness evidence of six commonly used anti-hepatic fibrosis Chinese patent medicines (CPMs), including Biejiajian Pill (BP), Dahuang Zhechong Pill (DZP), Biejia Ruangan Compound (BRC), Fuzheng Huayu Capsule (FHC), Anluo Huaxian Pill (AHP), and Heluo Shugan Capsule (HSC), using both Eff-iEC and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. The recognition of these CPMs within the TCM academic community was also assessed through their inclusion in relevant medical documents. Results showed that the evidence of BRC and FHC received higher assessments in both Eff-iEC and GRADE system, while the assessments for others varied. Analysis of community recognition revealed that Eff-iEC more accurately reflects the clinical value of these CPMs, exhibiting superior evaluative capabilities. By breaking through the conventional pattern of TCMs effectiveness evaluation, Eff-iEC offers a novel epistemology that better aligns with the clinical realities and reasoning of TCMs, providing a coherent methodology for clinical decision-making, new drug evaluations, and health policy formulation.

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: To describe screening situation and associated factors of early warning signals in psychopathology in kindergarten children, so as to provide reference for effective prevention and treatment strategies. Methods: From October to December 2021, a cross sectional study was conducted in kindergartens in Jinniu District of Chengdu. The age appropriate early warning signals were screened among 20 417 kindergarten children for developmental problems. Chi square test and Logistic regression were used for univariate and multivariate analysis of positive early warning signs screening, respectively. Results: The positive screening of early warning signals among 2 year old children was mainly distributed in the language domain (6.12%), personal social domain for 2.5 year old (1.70%) and 3 year old children (1.76%), and fine motor domain for 4 year old (2.04%) and 5 year old children (1.03%). Univariate and multivariate analysis showed that factors associated with positive screening of early warning signals included gender, family structure, history of diseases, electronic screen exposure >2 h/d, sleep duration at night, and age of children( P < 0.05). Conclusion It is necessary to carry out regular screening for early warning signals for psychopathology in kindergartens. Age appropriate health education for early childhood development should be carried out against nurses and kindergarten teaching staff. Similarly, more attention should be paid to healthy sleep and media usage to improve physical and mental wellness among preschool children.