Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective:To explore the spousal correlations of total cholesterol(TC),total triglyceride(TG),low-density lipoprotein cholesterol(LDL-C),and high-density lipoprotein cholesterol(HDL-C),and to investigate the reasons behind these spousal correlations.Methods:Participants and data were from the baseline survey of family-based cohort studies in Fangshan,Beijing and Tulou,Fujian.The ori-gin of spousal correlations were explored from perspectives of convergence,assortative mating,social ho-mogamy.Pearson's correlation and generalized linear models(GLM)were used to estimate the spousal correlation.Convergence was assessed by Pearson's correlation between the phenotypic differences be-tween couples and the duration of marriage,with GLM used for further validation.Pearson's correlation of genetic risk scores(GRS)and couple-specific Mendelian randomization(MR)were calculated to assess the genetic correlation and possible causal relationships between spouses.Two-independent-sample t-tests were used to compare GRS consistency across subgroups divided by education attainment,couple-specific MR and Q statistics used to test assortative mating in subgroups and intergroup differences.Results:In the study,342 couples(287 couples from Fangshan and 55 couples from Fujian)were included,with the average age of(64.91±8.76)years.Spousal correlations of TC,TG,HDL-C,and LDL-C showed statistically significant associations both before and after adjusting for covariates,with effect sizes of 0.229(95％CI:0.125-0.327),0.257(95％CI:0.155-0.354),0.179(95％CI:0.074-0.280),and 0.181(95％CI:0.076-0.282).For convergence,for each additional year of marriage,ΔTC increased by 0.016 mmol/L(95％CI:0.001-0.033 mmol/L),and ΔLDL-C increased by 0.017 mmol/L(95％CI:0.002-0.031 mmol/L).For assortative mating,GRS correlations and results of couple specific MR didn't show any statistical significance.For social homogamy,no differences in GRS or assortative mating were found between subgroups stratified by education attainment.Conclusion:The blood lipid in participants exhibit spousal phenotypic correlations,however,no effects of convergence,assortative mating or social homogamy were observed.More independent studies with larger sample sizes are warranted to further validate these findings in the future.

Objective To investigate the diagnostic value of multimodality-enhanced CT-based radiomics nomogram for muscle-inva-sive bladder urothelial carcinoma.Methods A retrospective analysis was performed on the preoperative data of 644 patients with pathologically confirmed bladder urothelial carcinoma from three medical centers.Region of interest(ROI)were drawn on preopera-tive contrast-enhanced CT images,and radiomics features were extracted.Patients from medical center 1 were randomly divided into training set and internal validation set in a 7∶3 ratio,while patients from medical centers 2 and 3 were combined as an external val-idation set.The diagnostic performance of the models was evaluated using receiver operating characteristic(ROC)curve.Results In the external validation set,the area under the curve(AUC)for diagnosing muscle-invasive bladder urothelial carcinoma using the multi-phase fusion radiomics model was 0.861[95％ confidence interval(CI)0.811-0.911].The nomogram constructed by combi-ning the multi-phase fusion radiomics model with clinical factors achieved an AUC of 0.901(95％ CI 0.862-0.939).Conclusion The nomogram combining multimodality-enhanced CT-based radiomics with clinical factors can effectively diagnose muscle-invasive bladder urothelial carcinoma.

Objective To investigate the effect of the Nogo receptor antagonist NEP1-40,administered via an exogenous route,on axonal regeneration in rats with spinal cord injury(SCI),and to explore its mecha-nism of action in the process of neural repair.Methods SD rats were divided into a sham surgery group(Group A),an injury group(Group B),and an injury+NEP1-40 treatment group(Group C).In Group A,only laminectomy was performed without spinal cord injury.Groups B and C were subjected to a clip-type SCI model.Group C received treatment with NEP1-40 based on the established SCI model.Hindlimb motor func-tion in the three groups was assessed using the BBB score at 1,3,7,and 14 days post-surgery.Real-time quan-titative PCR(qPCR)and Western blot were used to detect changes in gene and protein expression levels of growth-associated protein-43(GAP-43)and microtubule-associated protein-2(MAP-2),characteristic markers of axonal and dendritic regeneration.Immunofluorescence was employed to analyze NF-200 and BrdU double-labeling,and changes in the number of double-labeled positive cells were observed and analyzed.Results In group A rats,the BBB scores at various time points after surgery showed no significant change compared with preoperative scores.In groups B and C,the BBB scores on postoperative day 1 were obviously lower than pre-operative scores.From days 3 to 14 after surgery,the BBB scores partially recovered compared with postopera-tive day 1,though they remained lower than those in group A.However,on postoperative days 3,7,and 14,the BBB scores in group C were higher than those in group B(P<0.05).qPCR and Western blot results showed that compared with preoperative levels,GAP-43 and MAP-2 mRNA and protein expression in groups B and C at postoperative days 3,7,and 14 showed a trend of first decreasing and then increasing,and the expression in group C was consistently higher than in group B(P<0.05).The expression level of NogoA in group C showed an opposite trend to GAP-43 and MAP-2.Compared with preoperative levels,NogoA mRNA and pro-tein expression in group B rats decreased on postoperative days 1 and 3(P<0.05)and increased on days 7 and 14(P<0.05).Compared with preoperative levels,NogoA mRNA and protein expression in groups B and C also showed a trend of first decreasing and then increasing,but in group C,at all postoperative time points except day 1,it was lower than in group B(P<0.05).Immunofluorescence results showed that over time,the number of cells double-labeled with BrdU and NF-200 gradually increased,with the highest number observed in group C on postoperative day 14.Conclusion NEP1-40 promotes neurological repair in SCI,providing a new approach for SCI repair treatment.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Genomic imprinting refers to the phenomenon of differential expression of two alleles due to their different parental origins. Genes that produce genomic imprinting are usually called imprinted genes. The genetic effect caused by the presence of imprinted genes is called parent-of-origin effect. Parent-of-origin effect and genomic imprinting play important roles in the pathophysiological mechanism and occurrence and development of cardio-metabolic diseases. In-depth exploration of the law and potential roles of imprinted genes and parent-of-origin effects will help to better understand the mechanism of cardio-metabolic diseases, and also provide important theoretical basis for the precise treatment of diseases related to imprinted genes.

Genomic imprinting refers to the phenomenon of differential expression of two alleles due to their different parental origins. Genes that produce genomic imprinting are usually called imprinted genes. The genetic effect caused by the presence of imprinted genes is called parent-of-origin effect. Parent-of-origin effect and genomic imprinting play important roles in the pathophysiological mechanism and occurrence and development of cardio-metabolic diseases. In-depth exploration of the law and potential roles of imprinted genes and parent-of-origin effects will help to better understand the mechanism of cardio-metabolic diseases, and also provide important theoretical basis for the precise treatment of diseases related to imprinted genes.

Objective To investigate the diagnostic value of multimodality-enhanced CT-based radiomics nomogram for muscle-inva-sive bladder urothelial carcinoma.Methods A retrospective analysis was performed on the preoperative data of 644 patients with pathologically confirmed bladder urothelial carcinoma from three medical centers.Region of interest(ROI)were drawn on preopera-tive contrast-enhanced CT images,and radiomics features were extracted.Patients from medical center 1 were randomly divided into training set and internal validation set in a 7∶3 ratio,while patients from medical centers 2 and 3 were combined as an external val-idation set.The diagnostic performance of the models was evaluated using receiver operating characteristic(ROC)curve.Results In the external validation set,the area under the curve(AUC)for diagnosing muscle-invasive bladder urothelial carcinoma using the multi-phase fusion radiomics model was 0.861[95％ confidence interval(CI)0.811-0.911].The nomogram constructed by combi-ning the multi-phase fusion radiomics model with clinical factors achieved an AUC of 0.901(95％ CI 0.862-0.939).Conclusion The nomogram combining multimodality-enhanced CT-based radiomics with clinical factors can effectively diagnose muscle-invasive bladder urothelial carcinoma.

Objective:To explore the spousal correlations of total cholesterol(TC),total triglyceride(TG),low-density lipoprotein cholesterol(LDL-C),and high-density lipoprotein cholesterol(HDL-C),and to investigate the reasons behind these spousal correlations.Methods:Participants and data were from the baseline survey of family-based cohort studies in Fangshan,Beijing and Tulou,Fujian.The ori-gin of spousal correlations were explored from perspectives of convergence,assortative mating,social ho-mogamy.Pearson's correlation and generalized linear models(GLM)were used to estimate the spousal correlation.Convergence was assessed by Pearson's correlation between the phenotypic differences be-tween couples and the duration of marriage,with GLM used for further validation.Pearson's correlation of genetic risk scores(GRS)and couple-specific Mendelian randomization(MR)were calculated to assess the genetic correlation and possible causal relationships between spouses.Two-independent-sample t-tests were used to compare GRS consistency across subgroups divided by education attainment,couple-specific MR and Q statistics used to test assortative mating in subgroups and intergroup differences.Results:In the study,342 couples(287 couples from Fangshan and 55 couples from Fujian)were included,with the average age of(64.91±8.76)years.Spousal correlations of TC,TG,HDL-C,and LDL-C showed statistically significant associations both before and after adjusting for covariates,with effect sizes of 0.229(95％CI:0.125-0.327),0.257(95％CI:0.155-0.354),0.179(95％CI:0.074-0.280),and 0.181(95％CI:0.076-0.282).For convergence,for each additional year of marriage,ΔTC increased by 0.016 mmol/L(95％CI:0.001-0.033 mmol/L),and ΔLDL-C increased by 0.017 mmol/L(95％CI:0.002-0.031 mmol/L).For assortative mating,GRS correlations and results of couple specific MR didn't show any statistical significance.For social homogamy,no differences in GRS or assortative mating were found between subgroups stratified by education attainment.Conclusion:The blood lipid in participants exhibit spousal phenotypic correlations,however,no effects of convergence,assortative mating or social homogamy were observed.More independent studies with larger sample sizes are warranted to further validate these findings in the future.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.