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 characterize longitudinal trajectories of testicular development in boys and breast development in girls, so as to provide reference data for understanding patterns of pubertal sexual maturation. Methods: Based on the Shanghai Pudong New Area Cohort Study on Growth, Development and Health in Children and Adolescents, a baseline survey was conducted in 2020 using a mult stage cluster random sampling method. A total of 2 184 children who completed all follow ups during the primary school period from 13 elementary schools in Pudong New Area,Shanghai,with annual follow ups during 2021-2025. Testicular volume and Tanner stage of breast development were assessed by professional physicians using standardized visual inspection and palpation. The age distribution of testicular volume and breast development was fitted by using cumulative link mixed models and Turnbull s nonparametric maximum likelihood estimation method. Results: Median ages for testicular volumes of 2, 3, 4 and 5 mL in boys were 7.07, 9.24, 10.29, and 11.57 years old, respectively. Median ages for Tanner breast stages Ⅱ, Ⅲ, Ⅳ, and Ⅴ in girls were 8.55 , 10.17, 11.18, and 13.78 years old, respectively. Based on overweight and obesity, stratified analysis showed that earlier pubertal onset among overweight/obesity children, and the key milestones for pubertal initiation were testicular volume reaching 4 mL in boys and breast Tanner II in girls for 10.29, 10.83; 8.18, 9.00 years. Conclusion Overweight and obesity are associated with earlier pubertal initiation,but there are certain gender and developmental stage specific patterns.

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.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

ObjectiveTo investigate the mechanism underlying the effects of three formulas of the method of warming Yang and relieving depression on anxiety- and depression-like behaviors in the mice subjected to a two-hit paradigm of maternal separation （MS） combined with restraint stress （RS）. MethodsNeonatal mice were randomly assigned into the following groups： Blank control， MS alone， model （MS+RS）， warming Yang （WY，5.85 g·kg^-1）， relieving depression （RD，12.03 g·kg^-1）， warming Yang and relieving depression （WYRD，16.71 g·kg^-1）， and fluoxetine groups （2.6 mg·kg^-1）. MS was conducted on postnatal day 5 （PD5）. Weaning and preventive drug administration were carried out on PD21， and restraint stress was applied on PD90. Anxiety- and depression-like behaviors were assessed using the open field test （OFT）， the elevated zero maze test and tail suspension test （TST）. High-performance liquid chromatography with electrochemical detection （HPLC-ECD） was employed to measure the levels of neurotransmitters， including 5-hydroxytryptamine （5-HT） and dopamine （DA）， in the hippocampus. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression levels of 5-HT_1A receptor （5-HT_1AR）， serotonin transporter （SERT）， monoamine oxidase A （MAOA）， glucocorticoid receptor （GR）， adrenocorticotropic hormone （ACTH）， corticotropin-releasing hormone （CRH）， brain-derived neurotrophic factor （BDNF）， and tyrosine kinase receptor B （TrkB）. Immunohistochemistry （IHC） was applied to assess the protein expression levels of 5-HT_1AR， 5-HT_2A receptor （5-HT_2AR）， 5-HT_2C receptor （5-HT_2CR）， GR， and CRH receptor 1 （CRHR1）. The protein expression levels of BDNF and TrkB in the hippocampus were measured using the fully automated protein expression analysis system （Simple Wes）. ResultsIn the behavioral tests， compared with the blank control group， the model group exhibited significant increases in locomotor distance and central area crossings （P<0.05， P<0.01）in OFT， as well as a significant increase in immobility time in the TST （P<0.05）. Compared with the model group， the RD， WYRD， and fluoxetine groups all showed significantly reduced central area crossings （P<0.05， P<0.01）. Regarding neurotransmitter levels， compared with the blank control group， the model group had significantly decreased hippocampal epinephrine （E） and dopamine （DA） content （P<0.05， P<0.01）. Compared with the MS group， the model group showed significant decreases in hippocampal norepinephrine （NE）， E， and 5-HT content （P<0.05， P<0.01）. Compared with the model group， the WY group decreased hippocampal NE， DA， and 5-HT content （P<0.01）， while the RD， WYRD， and fluoxetine groups exhibited significantly increased hippocampal E content （P<0.01）. At the molecular level， compared with the blank control and MS groups， the model group showed significant increases in mRNA levels of 5-HT_1AR， SERT， MAOA， ACTH， and CRH （P<0.01）， and significant decreases in mRNA levels of tryptophan hydroxylase （TPH）， GR， BDNF， and TrkB （P<0.01）. Protein expression of 5-HT_1AR， 5-HT_2AR， and 5-HT_2CR was also significantly elevated （P<0.05， P<0.01） in the model group. Compared with the model group， the RD group showed significant decreases in SERT， MAOA， and CRH mRNA （P<0.05， P<0.01）， significant increases in TPH， BDNF， and TrkB mRNA （P<0.01）， significantly reduced protein expression of 5-HT_1AR， 5-HT_2AR， and 5-HT_2CR （P<0.05）， and significantly increased GR protein expression. The WY group showed a significant increase in ACTH mRNA expression （P<0.01）. The fluoxetine group exhibited significant decreases in MAOA and CRH mRNA （P<0.05， P<0.01）， significant increases in TPH， BDNF， and TrkB mRNA （P<0.01）， significantly reduced 5-HT_2AR protein expression （P<0.01）， and significantly increased GR protein expression（P<0.05）. ConclusionThe method of warming Yang and relieving depression can ameliorate anxiety- and depression-like behaviors in two-hit mice. Its mechanism may be associated with the BDNF/TrkB signaling pathway， as well as the HPA axis and serotonergic system.

Objective: To determine the potential genetic causal relationship between sleep related phenotypes and female pubertal onset using the two sample Mendelian randomization approach. Methods: Genetic instrumental variables for sleep and age at menarche were sourced from FinnGen, IEU Open GWAS and GWAS Catalog three publicly available Genome Wide Association Study databases. Causal effects were estimated using the inverse variance weighted method, supplemented by sensitivity analyses and tests for heterogeneity, pleiotropy, and direction. Results: Genetically predicted sleep duration was positively associated with age at menarche ( β =0.61, 95% CI =0.30-0.91, P <0.01). Conversely, overall sleep problems ( β =-0.13, 95% CI =-0.20 to -0.07 ), two specific types of sleep disorders (sleep apnea: β =-0.10, 95% CI =-0.16 to -0.03; insomnia: β =-0.40, 95% CI = -0.69 to -0.11) were causally associated with earlier age at menarche (all P <0.01). Sensitivity analyses confirmed the robustness of these findings with consistent effect directions and no evidence of significant directional pleiotropy. Conclusions The study reveales a genetic causal relationship between sleep and pubertal onset. Adequate sleep duration appears to have a protective effect. These findings suggest that sleep may have a critical regulatory role in the initiation of pubertal development.

Objective: To establish a risk assessment system for infectious disease prevention and control in schools in Shangcheng District, Hangzhou and determine risk levels for each school, and propose corresponding risk management measures, so as to provide a scientific reference for infectious disease prevention and control in primary and secondary schools. Methods: Based on the Failure Mode and Effects Analysis (FMEA) method, potential failure analysis and current situation investigation of infectious disease prevention and control risks were conducted in 110 primary and secondary schools from 2022 to 2024 in Shangcheng District, Hangzhou. Risk levels were classified using K-Means cluster analysis. Results: Through expert panel discussions using FMEA, 6 first level indicators and 28 second level indicators were identified. The top three risk priority numbers were implementation of required prevention and control measures for clustered infectious disease outbreaks in schools in the past three years ( 189.00 ), student morning/afternoon health checks (168.00), and reporting status of clustered infectious disease outbreaks in schools in the past three years (144.00). The comprehensive prevention scores of schools ranged from 61.00 to 98.00 (mean: 87.40 ). There were no statistically significant differences in the average scores(primary school: 88.17±7.39, nine year consistent education: 86.26±7.68, junior high school: 85.55±8.20, and high school: 88.72±4.91) and risk level distribution of schools with different educational stages( F/H=0.95,1.47, P >0.05).K-Means cluster analysis divided the schools into 5 risk levels with cluster centers at 93.25, 85.78, 79.69, 70.29, 61.00 ( F=309.21, P <0.05), with 80% of schools classified as low risk or below. Conclusion The infectious disease prevention and control risk assessment system for primary and secondary schools can be established, and hierarchical management can be conducted according to school risk levels, thereby improving the efficiency and effectiveness of school infectious disease prevention and control, and enhancing the precision and sustainability of prevention efforts.

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.

ObjectiveTo investigate whether there are differences in the efficacy of Gegen Qinliantang with different contents of Puerariae Lobatae Radix on the acute enteritis （AE） model mice and provide a scientific basis for the interpretation of Gegen Qinliantang in the treatment of "Xie Re Li". MethodsA total of 112 male BALB/c mice were randomly divided into a blank group，model group，single Puerariae Lobatae Radix group，non-Puerariae Lobatae Radix group，regular dose Gegen Qinliantang group （regular dose group），half-dose Puerariae Lobatae Radix group，and doubled-dose Puerariae Lobatae Radix group， with 16 mice in each group. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of the colon tissue. Western blot was employed to detect the expression of ZO-1 （a protein in the tight junction） and Occludin in the colon tissue， as well as the changes of tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）. ResultsCompared with the blank group，the DAI scores of the mice in the model group were significantly higher （P<0.05），and the histopathological sections of their colon tissues showed mucosal damage，glandular atrophy，disordered arrangement，and a large number of inflammatory cells infiltration，and the expression of ZO-1 and Occludin proteins in their colon tissues was significantly down-regulated （P<0.05，P<0.01）. The expression of inflammatory factors TNF-α and IL-1β was significantly up-regulated （P<0.05，P<0.01）. Compared with the model group，the DAI scores of mice in all dosing groups decreased significantly （P<0.05），with the most significant effect in the regular dose group. After 7 d of drug administration，the regular dose group had the best impact on the repair of colonic mucosa in the AE mouse model. The regular dose group significantly down-regulated the expression of TNF-α （P<0.05） and significantly up-regulated the expression of ZO-1 protein （P<0.05）. The doubled-dose Puerariae Lobatae Radix group significantly down-regulated the expression of IL-1β protein （P<0.01），and there was no significant difference between all dosing groups and the model group in terms of the expression of Occludin protein. After 14 d of drug administration，the best effect on the repair of colonic mucosa in the AE mouse model was observed in the doubled dose Puerariae Lobatae Radix group. All groups except the non-Puerariae Lobatae Radix group significantly down-regulated the expression of TNF-α （P<0.01）. Meanwhile，the regular dose group and doubled-dose Puerariae Lobatae Radix group significantly elevated the expression level of Occludin protein （P<0.01）. The doubled-dose Puerariae Lobatae Radix group also significantly inhibited the expression of IL-1β protein （P<0.05） and up-regulated ZO-1 protein expression （P<0.05）. ConclusionGegen Qinliantang can reduce the pathological damage of colon tissue， protect the barrier function and structure of intestinal epithelial cells， and reduce the expression of inflammatory factors， so as to achieve the therapeutic effect of AE model mice. When comparing the therapeutic efficacy of Gegen Qinliantang containing different Gegen contents， Gegen Qinliantang with the proportion of the original formula of Zhongjing was the most effective in AE model mice.