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.

Objective To investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

ObjectiveTo conduct sarcopenia-related screening and assessment among community-dwelling elderly patients with type 2 diabetes mellitus (T2DM) in Changning District of Shanghai, to analyze the current status and influencing factors of suspected sarcopenia in this population, and to provide references for the early prevention and treatment of sarcopenia. MethodsUsing a stratified random sampling method, elderly T2DM patients under management in the Chronic Disease Health Management System of Changning District in Shanghai were selected as the study subjects. Patients were screened for sarcopenia and underwent related health examinations according to the Chinese Expert Consensus on Diagnosis and Treatment of Sarcopenia in the Elderly (2021). Self-designed questionnaires were used to collect basic information, lifestyle habits and health status, and the Montreal Cognitive Assessment (MoCA) was employed to evaluate cognitive function. ResultsAmong 758 elderly patients with T2DM [average age: (72.19±6.19) years old], 214 patients were identified as having suspected sarcopenia,accounting for 28.23%. The proportion of suspected sarcopenia was higher in females (30.94%) than in males (24.08%) (χ²=4.20, P=0.040), and higher in older patients (χ²=22.26, P<0.001). Higher proportions of suspected sarcopenia were also observed among patients who followed a vegetarian diet (39.77%) (χ²=73.28, P=0.011), had diabetic foot (59.09%) (χ²=10.65, P=0.001), reported feeling fatigued most of the time (37.11%) (χ²=4.33, P=0.037), or had mild cognitive impairment (χ²=3.95, P=0.047). The duration of T2DM in the suspected sarcopenia group [(12.86±10.07) years old] was longer than that in the non-sarcopenia group [(9.73±8.63) years old] (t=3.79, P<0.001), and the average body mass index (BMI) in the suspected sarcopenia group [(22.17±2.45) kg⋅m^-²)] was lower than that in the non-sarcopenia group [(25.11±3.26) kg⋅m^-²)] (t=-11.41, P<0.001). Multivariate logistic regression analyses showed that older age (OR=1.08, 95%CI:1.047‒1.110) and longer duration of T2DM (OR=1.02, 95%CI:1.005‒1.043) were associated with a higher risk of suspected sarcopenia in elderly T2DM patients. Female elderly T2DM patients had a higher risk of suspected sarcopenia (OR=1.89, 95%CI:1.310‒2.733) than males, and those with diabetic foot (OR=3.66, 95%CI:1.341‒9.990) had a higher risk than those without diabetic foot. ConclusionThe proportion of elderly T2DM patients with suspected sarcopenia was relatively high. In community-based integrated health management, healthcare providers should pay more attention to elderly T2DM patients who are older, have longer disease duration, are female, or have diabetic foot, so as to identify risks early and implement targeted comprehensive interventions and health management.

ObjectiveTo explore the mechanism of Huanglian Wendantang on damp-heat type diabetes enteropathy rats based on the G protein coupled bile acid receptor 5/glucagon like peptide-1 （TGR5/GLP-1） signaling pathway and intestinal flora. MethodsA total of 72 male Sprague Dawley （SD） rats were adaptively fed for one week. Twelve SD rats were randomly selected as a blank group and fed with an ordinary diet. The rest of the SD rats were fasted for 12 hours without water. A rat model with damp-heat type diabetes enteropathy was made by left intraperitoneal injection of streptozotocin （55 mg·kg^-1） and high sugar and high fat diet （20% sucrose solution + high fat diet） in a humid and hot environment （artificial climate box： temperature 30-34 ℃， relative humidity： 85%-95%）. After successful modeling， the rats were randomly divided into a model group， a metformin group （200 mg·kg^-1）， low-dose， medium-dose， and high-dose Huanglian Wendantang groups （7.10， 14.20， 28.39 g·kg^-1）， with 12 rats in each group. The normal group and the model group were orally administered with physiological saline once a day for 6 consecutive weeks. During the observation period， the weight and blood glucose levels of rats were measured and recorded weekly. After the administration， fresh feces were collected from rats， and 16S rRNA sequencing technology was used to study the differences and changes in intestinal flora among different groups. The levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum of rats were detected by enzyme-linked immunosorbent assay （ELISA）， and the pathological morphological changes of colon tissue were examined. The expression of TGR5 and GLP-1 in colon tissue was detected by immunohistochemistry， and the expression of TGR5 and GLP-1 proteins in colon tissue was measured by Western blot. ResultsCompared with the blank group， the model group showed a decrease in body weight， an increase in blood glucose， and significant damp-heat symptoms. The levels of IL-6 and TNF-α in serum were significantly increased （P<0.01）. The expression of TGR5 and GLP-1 was decreased （P<0.01）， and the pathogenic bacteria were increased. Compared with the model group， the treatment groups exhibited improvements in body weight， blood glucose levels， and damp-heat syndrome in rats. Among them， the high-dose group of Huanglian Wendantang displayed the most significant improvement effect， with significantly reduced inflammation levels （P<0.01） and elevated expression of TGR5 and GLP-1 （P<0.01）. Colonic pathological sections showed that Huanglian Wendantang could effectively ameliorate colonic pathological changes. The 16S rRNA sequencing result indicated a significant increase in beneficial bacteria in the treatment groups. ConclusionHuanglian Wendantang can effectively ameliorate the damp-heat symptoms and blood glucose levels in rats with damp-heat type diabetes enteropathy， and it may exert an effect by regulating the TGR5/GLP-1 signaling pathway and intestinal flora disorder.

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.

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 evaluate the association between heatwaves and fall-related mortality.Methods:A total of 61 421 fall-related mortality from 2013 to 2022 in 7 provinces of China were included in a time-stratified case-crossover design, with daily meteorological data derived from the fifth generation European Reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts. Conditional logistic regression chimeric distributed lag nonlinear model was used to analyze the association between heatwaves and fall-related mortality and stratified analysis was conducted according to gender and age.Results:Heatwaves were associated with an increased risk of fall-related morality. The risk of fall-related mortality during heatwaves was higher than during non-heatwave periods ( OR=1.11, 95% CI: 1.05-1.18). The attributable fraction of fall-related motality due to heatwaves was 10.25% (95% CI: 4.49%-15.36%). For each 1 ℃ increase above the heatwave threshold, the risk of fall-related mortality increased by 34% ( OR=1.34, 95% CI: 1.02-1.76). The effect of heatwave duration on fall-related mortality was not statistically significant. Stratified analyses indicated that women experienced a higher risk of fall-related mortality during heatwaves ( OR=1.13, 95% CI: 1.04-1.22) compared to man ( OR=1.10, 95% CI: 1.04-1.17). Conclusions:Heatwave increases the risk of fall-related mortality, and the intensity of heatwaves modify this risk. Women are vulnerable populations.

Objective:To explore the correlation between renal function and abdominal aortic hemodynamic parameters based on four-dimensional flow(4D Flow) MRI in patients with chronic kidney disease (CKD).Methods:A cross-section prospective study was conducted on 73 patients diagnosed with CKD at First People′s Hospital of Changzhou between March 2021 and May 2023, as well as 13 volunteers without kidney injury. According to the estimated glomerular filtration rate (eGFR), the subjects were divided into CKD 1-3 stage group ( n=34), CKD 4-5 stage group ( n=39), and control group ( n=13). All subjects underwent 4D Flow MRI examination of the abdominal aorta, measuring pulse wave velocity (PWV), peak velocity, and maximum wall shear stress (WSS) at the proximal plane (Plane_1) and the higher renal artery opening plane (Plane_2) of the abdominal aorta. The differences in 4D Flow MRI hemodynamic parameters among the three groups were compared using a one-way analysis of variance or the Kruskal-Wallis test. The correlation between 4D Flow MRI hemodynamic parameters and eGFR was analyzed by using the Spearman correlation coefficient. The independent influencing factors that affect eGFR were analyzed by using multivariate linear regression analysis. Results:There were significant differences in abdominal aortic PWV and maximal WSS of Plane_1 and Plane_2 among the three groups ( H=10.38, P=0.006; F=11.16, P<0.001; F=4.75, P=0.011). There were no significant differences in the peak velocity of Plane_1 and Plane_2 among the three groups (both P>0.05). Abdominal aortic PWV was negatively correlated with eGFR ( r s=-0.30, P=0.005). There was a positive correlation between the maximal WSS of Plane_1 and Plane_2 with eGFR ( r s=0.39, P<0.001; r s=0.29, P=0.006). Abdominal aortic PWV and maximal WSS of Plane_1 were independent influencing factors of eGFR (b=-4.32, P=0.018; b=132.23, P=0.004). Conclusions:There is an independent correlation between renal function and abdominal aortic hemodynamic parameters based on 4D Flow MRI in patients with CKD, and abdominal aortic PWV and maximal WSS of Plane_1 were independent influencing factors of eGFR.

Objective To elucidate the mechanism through which Gypenoside L inhibits the growth of colon cancer by modulating carnitine palmitoyltransferase 1B (CPT1B),a pivotal enzyme in the fatty acid metabolism pathway. Methods Through in vitro experiments,various concentrations of Gypenoside LI LI were applied to inter-vene in colon cancer RKO and SW620 cells. The effects of Gypenoside LI on these cells were comprehensively evalu-ated using the CCK-8 assay,wound healing assay,colony formation assay,and live-dead cell staining,focusing on its impact on cell proliferation,migration,and apoptosis. Additionally,a human colon cancer tissue microarray (TMA) was utilized in conjunction with multiplex fluorescence immunohistochemistry to analyze the expression of CPT1B in colon cancer and adjacent tissues. SW620 cells were transfected with siRNA,and the mRNA and protein expression levels of CPT1B post-transfection were assessed using quantitative real-time PCR (qPCR) and Western blotting. Furthermore,an in vivo nude mouse colon cancer model was established to investigate the inhibitory effect of Gypenoside LI LI on colon cancer growth. Results In vitro experiments demonstrated that Gypenoside LI LI effectively inhibited the proliferation and migration of RKO and SW620 cells in a concentration-and time-dependent manner. Additionally,multiple fluorescence immunohistochemistry analyses revealed that the expression level of CPT1B in colon cancer tissues was significantly higher than that in adjacent non-tumor tissues. Gypenoside LI LI promoted ROS accumulation by inhibiting CPT1B expression. In vivo experiments further confirmed that Gypenoside LI LI could inhibit tumor formation in nude mice and reduce CPT1B expression. Conclusions This study elucidates the mechanism by which Gypenoside LI inhibits the growth of colon cancer cells. Specifically,it downregulates CPT1B,leading to increased accumulation of reactive oxygen species (ROS),disruption of fatty acid oxidation metabolism,and ultimately inducing apoptosis in colon cancer cells. These findings offer valuable insights into colon cancer treatment,suggesting new therapeutic strategies and potential drug targets.