OBJECTIVE To investigate the improvement effects and mechanism of Achyranthes bidentata total saponins （ABS） extract on vascular endothelial dysfunction in spontaneously hypertensive rat （SHR） based on cytochrome P450 4A （CYP4A）/20-hydroxyeicosatetetraenoic acid （20-HETE）/G protein-coupled receptor 75 （GPR75） axis. METHODS Ten Wistar- Kyoto rats were taken as the normal control group. Forty SHR were first stratified by systolic blood pressure and then， within each stratum， randomly assigned using a random-number table to the model group （MOD group）， captopril positive control group （CAP group， 10 mg/kg）， ABS low- and high-dose extract groups （ABS-L group， ABS-H group， 60 and 120 mg/kg）， with 10 rats in each group. Animals in each group were given the corresponding drug or equal volume of pure water by gavage， once a day， for 28 consecutive days. After the last administration， systolic blood pressure of rats was measured. The levels of vasoactive substances， inflammatory factors and oxidative stress indicators in serum were measured. The pathological changes of rat thoracic aorta were observed. The level of reactive oxygen species （ROS） in aortic tissue was analyzed. The expressions of endothelial nitric oxide synthase （eNOS）， CYP4A， GPR75， nuclear factor-κB p65 （NF-κB p65）， phosphorylated NF-κB p65， p22phox， and reduced nicotinamide adenine dinucleotide phosphate oxidase 4（NOX4） in thoracic aorta tissue were detected. RESULTS After 28 d of treatment， compared with MOD group， the systolic blood pressure of rats in the ABS-L and ABS-H groups decreased significantly. The levels of 20-HETE， angiotensin Ⅱ， interleukin-1β， interleukin-6， tumor necrosis factor-α， intercellular cell adhesion molecule-1 and malondialdehyde in serum were significantly reduced （P＜0.05 or P＜0.01）， while the levels of nitric oxide， superoxide dismutase， glutathione peroxidase and catalase were significantly increased （P＜0.05 or P＜0.01）. Intimal damage of thoracic aorta was reduced， and endothelial cell morphology was improved. The expressions of ROS， CYP4A， GPR75， p22phox， NOX4 and the phosphorylation level of NF-κB p65 protein in thoracic aorta were down-regulated or reduced （P＜0.05 or P＜0.01）， while the expression of eNOS was up-regulated （P＜0.05 or P＜0.01）. CONCLUSIONS ABS extract may alleviate the inflammatory response and oxidative stress in SHR effectively by down-regulating the expression of CYP4A， reducing the production of 20-HETE， inhibiting the activation of GPR75， and subsequently suppressing the activation of downstream NF-κB and NOX4， thereby improving hypertension-related vascular endothelial dysfunction.

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.

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.

AIM To investigate the promotional effects of esculin combined with bone marrow mesenchymal stem cells(BM-MSCs)transplantation on the repair of spinal cord injury(SCI)in rats.METHODS The rats were randomly divided into the sham operation group,the model group,the esculin group for gavage of 20 mg/kg esculin,the BM-MSCs group for tail vein injection of 1 mL of 1×106/mL BM-MSCs,and the combinaiton treatment group.The SCI rat model was established using Allen's method,followed by the 14 days consecutive corresponding drug administration starting from the 2nd day after modeling.On days 3,7 and 14 of drug administration,the rats had their hind limbs motor function evaluated by the BBB scoring;and their footprint experiment conducted on the 14th day after modeling.After 14 days of administration,the rats had their morphological changes of spinal cord tissue observed with HE staining and Nissl staining;their activities of SOD and GSH,and level of MDA in spinal cord tissue detected by kits;their expressions of MAP2,GAP43 and GFAP in spinal cord tissue detected by immunofluorescence;and their expressions of NQO-1,Nrf-2,Bcl-2 and Bax proteins in spinal cord tissue detected by Western blot.RESULTS Compared with the model group,the groups interved with esculin,or BM-MSCs,or the combination treatment showed improvements in hind limb function and spinal cord tissue morphology(P＜0.05);decreased MDA levels(P＜0.05);increased SOD and GSH activities(P＜0.05);increased MAP2 and GAP43 fluorescence intensity(P＜0.05);decreased GFAP fluorescence intensity(P＜0.05);increased NQO-1,Nrf-2 and Bcl-2 protein expressions(P＜0.05);and decreased Bax protein expression(P＜0.05).And the combination treatment group was observed with an even better effects(P＜0.05).CONCLUSION The combination of esculin and BM-MSCs transplantation can effectively improve the spinal cord tissue damage and hind limb function in SCI rats.This effect may be achieved by activating the Nrf-2/NQO-1 signaling pathway to inhibit oxidative stress response,thereby reducing neuronal apoptosis,blocking glial scar formation,and promoting stem cell differentiation to rebuild neurons.

Objective:To explore the deubiquitination modification of the deubiquitinase BRCC3 in rats with neuropathic pain (NPP) treated with meridian tuina of Zhuang medicine and its analgesic molecular mechanism.Methods:Rats were divided into normal group, sham-operation group, model group, sham-tuina group, and meridian tuina of Zhuang medicine group using a random number table method, with 9 rats in each group. Except for the normal and sham operation groups, spinal nerve ligation models were prepared in all other groups. On the first day after surgery, intervention was carried out on the meridian tuina of Zhuang medicine and sham-tuina groups for 14 days, while the other three groups were not intervened; the paw withdrawal mechanical threshold (PWMT) and paw withdrawal thermal latency (PWTL) of each group of rats were measured at 0 days before operation, 1, 7, and 14 days after operation. Western blot was used to detect the expressions of BRCC3 and NLRP3 proteins in spinal cord tissue, and ELISA was used to detect the level of IL-1β in serum.Results:On postoperative 7 and 14 d, compared with the model group, the meridian tuina of Zhuang medicine group showed an increase in PWMT and PWTL, a decrease in NLPR3 and BRCC3 expression in spinal cord tissue, and a decrease in serum IL-1β levels ( P<0.05). Compared with the sham-tuina group, the meridian tuina of Zhuang medicine group showed an increase in PWMT and PWTL, a decrease in NLRP3 protein expression in spinal cord tissue, and a decrease in IL-1β levels in serum ( P<0.05). Conclusion:Meridian tuina of Zhuang medicine can alleviate pain sensitivity in SNL model rats, and its mechanism is related to the inhibition of the expression of deubiquitinase BRCC3 by meridian massage of Zhuang medicine, which increases the ubiquitination level of NLRP3 and hinders its activation, thereby blocking the immune inflammatory response mediated by inflammatory factors.

[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the prevalence and risk factors of overweight and obesity among Chinese children aged 3-18 years from 11 provinces, antonomous regions, or municipalities.Methods:This national cross-sectional community health survey utilized a multistage stratified cluster-random sampling method to recruit 193 997 nationally representative participants from 11 provinces, autonomous regions, or municipalities between January 2017 and December 2019. All participants underwent physical examinations, and their caregivers completed questionnaires assessing participants′ dietary, lifestyle, familial, and perinatal information. Multilevel multinomial logistic regression models were employed to identify the potential risk factors.Results:The cohort comprised 193 997 children (102 178 boys, 91 819 girls),aged (10±4) years. Overall prevalence rates were 30 574(15.8%)overweight children and 17 217(8.9%) obesity children. Boys exhibited higher overweight and obesity rates than girls (17.0% (17 368/102 178) vs. 14.4% (13 206/102 178), 11.3% (11 553/91 819) vs. 6.2% (5 664/91 819), χ2=249.12,1 578.69,both P<0.001). The detection rates of obesity in Tanner stage 2 and 3 were the highest in boys and girls, with 13.4%(2 231/16 665) and 8.6%(880/10 221) respectively. Risk factors for obesity included parental overweight (paternal OR=2.34 and maternal OR=2.29), annual household income of 100 000-200 000 yuan (compared with<100 000 yuan, OR=1.04), higher paternal education (compared with below high school,high school and a college education OR=1.09,1.14), birth weight >4.0 kg (≤5 and>5 years old OR=1.74, 1.44,respectively), and western food consumption≥1 time/month (compared with<1, 1-2, 3-4,>4 times/month OR=1.36, 1.30, 1.67(≤5 years), 1.19, 1.16, 1.15 (>5 years), respectively) (all P<0.05). Conversely, coarse grain intake≥1 times/week (compared with<1 times/week, every day, 3-4, 1-2 times/week OR=0.74, 0.80, 0.71 (≤5 years), 0.75, 0.87, 0.90(>5 years), respectively, all P<0.05) was associated with reduced obesity risk. Conclusions:Obesity epidemiology in children demonstrates significant heterogeneity across age, gender, geographic regions, and pubertal stages. It is necessary to establish a personalized prevention and control strategy.

Objective:To analyze the clinical and ultrasonic features of breast invasive ductal carcinoma in young women patients,and improve the accuracy of ultrasonic diagnosis of breast invasive ductal carcinoma in young patients.Methods:A retrospective study was conducted on patients with invasive ductal carcinoma who underwent surgery in the Second Hospital of Shandong University from December 2015 to June 2023,with complete clinical data,preopera-tive ultrasonography and postoperative pathology.All patients were divided into two groups:young group(≤35 year-old)and control group(>35 year-old).Univariate t-test and multivariate Logistic regression analyses were used to assess the factors.Results:There was a statistical difference between the young group and the control group in the accuracy of ultrasound diagnosis(young group 81%vs control group 94%,χ2=13.082,P=0.001).Compared with the control group,the young group had a shorter disease history,a higher proportion of multiple masses,posterior echo changes and hypo-vascular lesions(P<0.05).However,there was no statistical difference in family history of breast cancer,other cancer family history,clinical symptoms,size,lesion classification,palpation,location,aspect ratio,focus echo,shape,edge per-formance,bright ring sign,calcification characteristics,ER,PR,HER-2 and Ki67(P>0.05).Conclusion:The incidence rate of young breast IDC is low,while the misdiagnosis rate of ultrasound is relatively high.More attention should be paid when the patient has a short history,multiple masses,changes in the posterior echo and less lesion blood supply.