With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:This study aims to investigate sex-specific abnormalities?? in gray matter volume (GMV) in Children with autism spectrum disorder (ASD) and their associations with gene expression.Methods:T 1-weighted brain MRI data were collected at the MRI Center of the University of Electronic Science and Technology of China between 2022 and 2023 from 100 children with ASD and 90 typically developing (TD) children. Voxel-based morphometry (VBM) was used to explore GMV differences between ASD boys and TD boys, and between ASD girls and TD girls. Partial least squares regression (PLSR) was performed based on the Allen Human Brain Atlas to identify genes associated with GMV alterations, followed by enrichment analyses. Cell-type-specific expression analyses were used to examine associations across developmental stages and brain structures. Protein-protein interaction (PPI) networks were constructed to identify hub proteins. Results:Compared to TD boys, ASD boys showed increased GMV in the right superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, temporal pole, parahippocampal gyrus, fusiform gyrus, cuneus, and precuneus, as well as in the bilateral orbital part of the superior frontal gyrus and the gyrus rectus. Decreased GMV was observed in the cerebellar vermis and bilateral cerebellar hemispheres. A total of 635 genes were associated with these GMV alterations, enriched in pathways related to DNA-templated transcription, RNA metabolism and biogenesis, and ion binding. Developmental analysis indicated strong associations with the cerebellum during early, middle-to-late childhood, and adolescence, and with the cerebral cortex in early adulthood. Protein-protein interaction (PPI) network analysis highlighted NOB1, GNL3L, ESF1, TFB2M, and WDR75 as specific hub proteins. Compared to TD girls, ASD girls exhibited increased GMV in the right middle and inferior temporal gyri, temporal pole, and fusiform gyrus, and decreased GMV in the cerebellar vermis and bilateral cerebellar hemispheres. A total of 765 genes were associated, enriched in pathways related to ion channel activity, signal transduction, and regulation of membrane potential. These genes showed strong associations with the amygdala during mid-to-late fetal development, middle-to-late childhood, adolescence, and early adulthood; with the cerebellum during late infancy, early childhood, and early adulthood; with the cerebral cortex during the mid-to-late fetal development, early neonatal period, and adolescence; with the hippocampus during middle-to-late childhood and adolescence; with the striatum during adolescence and early adulthood; and with the thalamus during early-to-mid fetal development, early neonatal period, and early adulthood. PPI network analysis identified ANK3, ANK1, SCN4B, NFKB1, and PXN as specific hub proteins. Conclusion:Both ASD boys and ASD girls exhibit GMV abnormalities compared with TD controls. The specific genes associated with GMV alterations are enriched in distinct biological pathways in boys and girls. Cell-type-specific expression analyses further revealed sex-dependent differences in developmental timing and brain structural correlations, and distinct PPI networks were constructed for each group.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:This study aims to investigate sex-specific abnormalities?? in gray matter volume (GMV) in Children with autism spectrum disorder (ASD) and their associations with gene expression.Methods:T 1-weighted brain MRI data were collected at the MRI Center of the University of Electronic Science and Technology of China between 2022 and 2023 from 100 children with ASD and 90 typically developing (TD) children. Voxel-based morphometry (VBM) was used to explore GMV differences between ASD boys and TD boys, and between ASD girls and TD girls. Partial least squares regression (PLSR) was performed based on the Allen Human Brain Atlas to identify genes associated with GMV alterations, followed by enrichment analyses. Cell-type-specific expression analyses were used to examine associations across developmental stages and brain structures. Protein-protein interaction (PPI) networks were constructed to identify hub proteins. Results:Compared to TD boys, ASD boys showed increased GMV in the right superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, temporal pole, parahippocampal gyrus, fusiform gyrus, cuneus, and precuneus, as well as in the bilateral orbital part of the superior frontal gyrus and the gyrus rectus. Decreased GMV was observed in the cerebellar vermis and bilateral cerebellar hemispheres. A total of 635 genes were associated with these GMV alterations, enriched in pathways related to DNA-templated transcription, RNA metabolism and biogenesis, and ion binding. Developmental analysis indicated strong associations with the cerebellum during early, middle-to-late childhood, and adolescence, and with the cerebral cortex in early adulthood. Protein-protein interaction (PPI) network analysis highlighted NOB1, GNL3L, ESF1, TFB2M, and WDR75 as specific hub proteins. Compared to TD girls, ASD girls exhibited increased GMV in the right middle and inferior temporal gyri, temporal pole, and fusiform gyrus, and decreased GMV in the cerebellar vermis and bilateral cerebellar hemispheres. A total of 765 genes were associated, enriched in pathways related to ion channel activity, signal transduction, and regulation of membrane potential. These genes showed strong associations with the amygdala during mid-to-late fetal development, middle-to-late childhood, adolescence, and early adulthood; with the cerebellum during late infancy, early childhood, and early adulthood; with the cerebral cortex during the mid-to-late fetal development, early neonatal period, and adolescence; with the hippocampus during middle-to-late childhood and adolescence; with the striatum during adolescence and early adulthood; and with the thalamus during early-to-mid fetal development, early neonatal period, and early adulthood. PPI network analysis identified ANK3, ANK1, SCN4B, NFKB1, and PXN as specific hub proteins. Conclusion:Both ASD boys and ASD girls exhibit GMV abnormalities compared with TD controls. The specific genes associated with GMV alterations are enriched in distinct biological pathways in boys and girls. Cell-type-specific expression analyses further revealed sex-dependent differences in developmental timing and brain structural correlations, and distinct PPI networks were constructed for each group.

Trace elements (TEs), also known as micronutrients in biology, are trace components required by the human body, accounting for 0.005% to 0.01% of body weight. Although TEs are present in small quantities in the human body, they play significant roles in cellular metabolism, enzyme activity regulation, immune function, nerve conduction, and bone health. In this review, the effects of TEs (zinc, iron, magnesium, selenium, copper, chromium, and manganese) for modulating biological functions on organisms are comprehensively analyzed and summarized. The mechanisms of various TEs in immune system, enzymatic reaction, oxidative stress, physical growth, and blood glucose regulation are deeply discussed, emphasizing the indispensable role of TEs in maintaining normal physiological functions of body. In addition, the future research directions of TEs are also prospected, including the mechanism of action, intake, metabolism, and storage of TEs at the cellular level. This review will provide useful information to further understand the biological effects and the application of TEs.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Efficiency in pharmacodynamic study and evaluation is the critical issue in current drug research and development of non-alcoholic steatohepatitis(NASH).Resmetirom,the first marketed medicine for NASH,is approved by pathological surrogate endpoints,meanwhile several clinical trials suspended due to failure to achieve the liver histologic surrogate endpoints.The well-done non-clinical pharmacodynamic study basing on pathological features(ballooning degeneration,lobular inflammation,fibrosis)of NASH,is a great support to the whole research and development projects of new medicines for NASH.In this article,we discussed the necessity and feasibility of the NASH non-clinical pharmacodynamic study combining the clinical trials of NASH drug,the pathological features and the animal models of NASH,in order to facilitate the high-quality research and development of NASH drugs.

Objective To investigate the associations of reproductive health indicators with lung function and chronic obstructive pulmonary disease(COPD)among women aged 40 years and above.Methods From Jul to Sep,2021,female subjects aged 40 years and above were randomly selected from the Shanghai Suburban Adult Cohort and Biobank for COPD screening.A questionnaire was used to obtain information on demographic characteristics and reproductive health indicators.Linear regression was used to analyze the effects of reproductive health indicators on forced vital capacity(FVC)and forced expiratory volume in the first second(FEV1).Logistic regression was also used to analyze the effects of reproductive health factors on FVC as a percentage of the predicted value(FVC%Pred)and FEV1%Pred as well as on COPD.Results A total of 1876 women aged 40 years and above were enrolled with mean age of(62.1±8.2)years old,among them,78.1%were menopausal,and 40.9%had been pregnant≥3 times.Multivariate analysis showed that FVC and FEV1 decreased in postmenopausal women,but menopause was not associated with a decrease in their percentage of predicted values.Pregnancies≥3 times was a risk factor for COPD(for 3 times,OR=4.92,95%CI:1.48-19.95,P<0.05;for≥4 times,OR=9.06,95%CI:2.32-41.57,P<0.01),while pregnancies of 2 times did not increase the risk of COPD.Conclusion In women aged 40 years and above,menopause is associated with poorer FVC and FEV1,and excessive pregnancy(≥3 times)is a risk factor for COPD.

This study was aimed at investigating the effect of lymphocyte activation gene-3(LAG3)on liver B lymphocyte subsets and their functions in WT and LAG3-KO mice infected with Echinococcus multilocularis(E.multilocularis).In a mouse model of E.multilocularis infection,the expression and localization of CD19 and α-SMA in liver were detected by immu nohistochemistry.CD80,CD86 and MHC-Ⅱ molecules expressed on B cells and their subsets in mice liver were detected by flow cytometry.After 12 weeks of infection,the area and percentage of CD19 in LAG3-KO group was slightly higher than that in WT group,but the difference was not statistically(t=-1.241、-1.237,P＞0.05).The area and percentage of a-SMA in LAG3-KO group was higher than that in WT group(t=-3.224、-3.227,P＜0.05).The proportion of CD80 and MHC-Ⅱ molecules expressed on liver B cells in LAG3-KO group was up-regulated(t=-2.379,-3.321,P＜0.05).The percentage of liver B2 cells in LAG3-KO group was higher than that in WT group(t=-2.695,P＜0.05).The expression of CD80 on Blb cells in LAG3-KO group was significantly up-regulated(t=-5.315,P＜0.001).The proportion of CD80 of B2 cells in LAG3-KO group was lower than that in WT group(t=2.806,P＜0.05).The expression of MHC-Ⅱ molecule in B2 cells in LAG3-KO group was up-regulated(t=-4.227,P＜0.01).It is suggested that LAG3 molecules affected the B cell subsets and func-tion of mouse liver in the middle stage of E.multilocularis infection,especially B2 lymphocytes.LAG3 molecule exerted an in-hibitory effect on the activation of B cells and the expression of MHC-class Ⅱ molecules,suggesting that it may be involved in B cell exhaustion caused by E.multilocularis.