ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

Objective: To analyze the impact of premature death due to four major chronic diseases on life expectancy in Yangpu District, Shanghai Municipality from 2010 to 2021, so as to provide the evidence for formulating chronic disease prevention and control strategies. Methods : Mortality data of registered residents in Yangpu District from 2010 to 2021 were collected through the Death Information Registration and Management System of the Shanghai Municipal Disease Control and Prevention Information Management Platform. The premature death probability of malignant tumors, diabetes, cardiovascular and cerebrovascular diseases, and chronic respiratory diseases, and life expectancy of residents were calculated using the abridged life table method. Trends in premature death probability for four types of chronic diseases were analyzed using the average annual percent change (AAPC). The impact of premature death probability due to four chronic diseases on life expectancy was assessed by Arriaga's decomposition method. Results : The premature death probability due to four major chronic diseases in Yangpu District decreased from 9.88% in 2010 to 9.22% in 2021, showing an overall declining trend (AAPC=-0.540%, P<0.05). Among females, the premature death probability declined from 6.71% to 4.90% (AAPC=-2.715%, P<0.05), whereas no statistically significant trend was observed in males (P>0.05). Life expectancy increased from 82.52 years in 2010 to 84.50 years in 2021, with an overall upward trend (AAPC=0.244%, P<0.05). Life expectancy rose by 1.71 years for males and 2.34 years for females (AAPC=0.197% and 0.303%,both P<0.05). Declines in premature death probability from malignant tumors (AAPC=-0.967%, P< 0.05) and chronic respiratory diseases (AAPC=-3.071%, P<0.05) contributed to gains in life expectancy of 0.30 years and 0.03 years, with contribution rates of 12.18% and 1.29%, respectively. Changes in premature death probability due to diabetes as well as cardiovascular and cerebrovascular diseases were not statistically significant (both P>0.05), resulting in reductions in life expectancy of 0.05 years and 0.10 years, with contribution rates of -2.40% and -5.05%, respectively. Notably, an increase in premature death probability due to cardiovascular and cerebrovascular diseases among males (AAPC=1.673%) contributed to a decrease of 0.22 years in male life expectancy, whereas a decrease among females (AAPC=-3.824%) contributed to an increase of 0.03 years in female life expectancy, with contribution rates of -13.03% and 1.14%, respectively. Conclusions From 2010 to 2021, Yangpu District experienced an overall decline in premature death probability due to four major chronic diseases and an increase in life expectancy. Greater attention should be paid to the negative impacts of premature death probability from diabetes as well as cardiovascular and cerebrovascular diseases among males on life expectancy.

BACKGROUND:BCR/ABL gene is a specific gene of Ph chromosome-positive acute lymphoblastic leukemia,and its expression level has become a sensitive indicator for monitoring minimal residual disease before and after allogeneic hematopoietic stem cell transplantation.However,whether the expression level of BCR/ABL gene before transplantation affects the efficacy of transplantation and how to guide the early intervention of relapse with tyrosine kinase inhibitors after transplantation is still inconclusive.OBJECTIVE:To explore the relationship between BCR/ABL gene expression and recurrence in patients with Ph chromosome positive acute lymphoblastic leukemia before and after related and allogeneic hematopoietic stem cell transplantation.METHODS:Twenty-four patients with Ph chromosome positive acute lymphoblastic leukemia who achieved complete hematological remission and underwent allogeneic hematopoietic stem cell transplantation were selected at the Affiliated Hospital of North China University of Science and Technology between January 2015 and December 2022.Real time fluorescence quantitative polymerase chain reaction was used to dynamically detect the expression levels of BCR/ABL genes during treatment,representing minimal residual disease.Based on BCR/ABL gene expression,tyrosine kinase inhibitors combined with chemotherapy was administered before transplantation to select the timing of allogeneic hematopoietic stem cell transplantation.After transplantation,the disease status was evaluated to guide the use of tyrosine kinase inhibitors,and an early intervention plan for recurrence was developed.RESULTS AND CONCLUSION:Follow-up was until December 2023,with a median follow-up time of 49(12-82)months.There were 8 cases of hematological recurrence,with a median recurrence time of 14(8-39)months and a cumulative recurrence rate of 33％(8/24).Univariate analysis showed that recurrence after allogeneic hematopoietic stem cell transplantation was not significantly correlated with gender,age,extramedullary complications,time from diagnosis to transplantation,HLA typing,acute graft-versus-host disease,and chronic graft-versus-host disease(P＞0.05).There was a significant correlation between the relief treatment course and minimal residual disease levels before transplantation.The second hematology completely resolution and positive minimal residual disease before transplantation had a higher hematological recurrence rate(P＜0.05).The 3-year cumulative recurrence rate,disease-free survival rate,and overall survival rate were 27％,63％,and 74％;the 5-year cumulative recurrence rate,disease-free survival rate,and overall survival rate were 38％,57％,and 74％,respectively.It is concluded that Ph chromosome positive acute lymphoblastic leukemia patients with BCR/ABL gene positive before transplantation have a higher recurrence rate.BCR/ABL gene expression after transplantation can guide the application of tyrosine kinase inhibitors and serve as a basis for early intervention in recurrence.

BACKGROUND:BCR/ABL gene is a specific gene of Ph chromosome-positive acute lymphoblastic leukemia,and its expression level has become a sensitive indicator for monitoring minimal residual disease before and after allogeneic hematopoietic stem cell transplantation.However,whether the expression level of BCR/ABL gene before transplantation affects the efficacy of transplantation and how to guide the early intervention of relapse with tyrosine kinase inhibitors after transplantation is still inconclusive.OBJECTIVE:To explore the relationship between BCR/ABL gene expression and recurrence in patients with Ph chromosome positive acute lymphoblastic leukemia before and after related and allogeneic hematopoietic stem cell transplantation.METHODS:Twenty-four patients with Ph chromosome positive acute lymphoblastic leukemia who achieved complete hematological remission and underwent allogeneic hematopoietic stem cell transplantation were selected at the Affiliated Hospital of North China University of Science and Technology between January 2015 and December 2022.Real time fluorescence quantitative polymerase chain reaction was used to dynamically detect the expression levels of BCR/ABL genes during treatment,representing minimal residual disease.Based on BCR/ABL gene expression,tyrosine kinase inhibitors combined with chemotherapy was administered before transplantation to select the timing of allogeneic hematopoietic stem cell transplantation.After transplantation,the disease status was evaluated to guide the use of tyrosine kinase inhibitors,and an early intervention plan for recurrence was developed.RESULTS AND CONCLUSION:Follow-up was until December 2023,with a median follow-up time of 49(12-82)months.There were 8 cases of hematological recurrence,with a median recurrence time of 14(8-39)months and a cumulative recurrence rate of 33％(8/24).Univariate analysis showed that recurrence after allogeneic hematopoietic stem cell transplantation was not significantly correlated with gender,age,extramedullary complications,time from diagnosis to transplantation,HLA typing,acute graft-versus-host disease,and chronic graft-versus-host disease(P＞0.05).There was a significant correlation between the relief treatment course and minimal residual disease levels before transplantation.The second hematology completely resolution and positive minimal residual disease before transplantation had a higher hematological recurrence rate(P＜0.05).The 3-year cumulative recurrence rate,disease-free survival rate,and overall survival rate were 27％,63％,and 74％;the 5-year cumulative recurrence rate,disease-free survival rate,and overall survival rate were 38％,57％,and 74％,respectively.It is concluded that Ph chromosome positive acute lymphoblastic leukemia patients with BCR/ABL gene positive before transplantation have a higher recurrence rate.BCR/ABL gene expression after transplantation can guide the application of tyrosine kinase inhibitors and serve as a basis for early intervention in recurrence.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

ObjectiveTo understand the incidence and mortality trends of acute cardiovascular and cerebrovascular events in Yangpu District of Shanghai from 2009 to 2022, and provide a basis for the prevention and control of cardiovascular and cerebrovascular events. MethodsData were obtained from the Shanghai Acute Cardiovascular and Cerebrovascular Events Surveillance Platform. Data on the incidence and mortality of cardiovascular and cerebrovascular events in the population (age group, gender) from 2009 to 2022 were collected, and the Joinpoint Regression Program version 4.9 was used to calculate the average annual percent change (AAPC) in the incidence and mortality rates of acute cardiovascular and cerebrovascular events. ResultsFrom 2009 to 2022, the crude incidence and standardized incidence rate of acute cardiovascular and cerebrovascular events in Yangpu District showed no significant changes (AAPC=1.41%, P=0.569; AAPC=-1.03%, P=0.675), the crude mortality rate of acute cardiovascular and cerebrovascular events in Yangpu District did not change significantly (AAPC=-3.04%, P=0.213), while the standardized mortality rate showed a decreasing trend (AAPC=-6.23% P=0.014). From 2009 to 2022, the crude incidence rates and age-standardized incidence rates for both males and females in Yangpu District showed no significant changes. The crude mortality trends for both genders were not significant, while the age-standardized mortality rates showed a decline (AAPC=-5.33%, P=0.029; AAPC=-7.50%, P=0.006). The incidence rate and age-standardized incidence rate were higher in males than in females. The crude incidence rates in the 30‒, 40‒, and 45‒year-old age groups increased annually (AAPC=9.13%、7.11%、4.67%, all P=0.001), and the crude mortality ratse in the 60‒, 65‒, 70‒, 75‒, 80‒, and 85‒year-old age groups declined annually (AAPC=-4.24%, P=0.044; AAPC=-5.41%, P=0.028; AAPC=-6.73%, P=0.004; AAPC=-7.46%, P=0.002; AAPC=-8.24%, P=0.002; AAPC=-6.16%, P=0.035). ConclusionFrom 2009 to 2022, the crude incidence, standardized incidence rate and crude mortality rate of cardiovascular and cerebrovascular events in Yangpu District tended to be stable, and the standardized mortality rate showed a downward trend. Men, middle-aged and young people were the key groups in the prevention and treatment of cardiovascular and cerebrovascular diseases, and it should be continued to improve the ability of medical emergency and increase the integration of medical and prevention.

Objective : To construct a humanized mouse model of the interleukin-9 receptor(IL-9R) coding DNA sequence(CDS) gene and to verify the genotype and IL-9R expression in mice. Methods : The CRISPR/Cas9 genome editing technology was used to replace the exon 2-7 fragment of the il-9r gene in mouse embryonic stem cells with the corresponding human IL-9R sequence. After verifying the completion of the gene fragment replacement, tetraploid embryos were constructed and microinjected back into the oviducts of surrogate mice. Through surrogacy by female mice, homozygous humanized mice were obtained. DNA was extracted from the homozygous humanized mice IL-9R CDS gene, and their genotypes were identified by agarose gel electrophoresis after PCR amplification. Western blot was used to detect the expression of IL-9R in the spleen and thymus of homozygous humanized mice with either wild-type(WT) or IL-9R gene humanization. Results : Gel electrophoresis after PCR amplification showed that mice with only a 1 805 bp band amplified using WT primers were wild-type, while mice with 2 553 bp and 2 340 bp bands amplified using 5KI and 3KI primers, respectively, were homozygous humanized mice with IL-9R CDS gene. Western blot results indicated that the tissues of homozygous humanized mice model with IL-9R CDS gene expressed IL-9R significantly. Conclusion The humanized mouse model with IL-9R CDS gene has been successfully constructed and characterized.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.