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 explore the ameliorative effect of Tiaozhi Xiaoban Mixture on atherosclerosis and the potential role of long non-coding RNA(Linc RNA)in anti-atherosclerosis.METHODS A model of atherosclerosis was established in SD rats subjec-ted to a high-fat diet.At 4 weeks post-modeling,thoracic aortic tissues from atherosclerotic rats were collected for hematoxylin-eosin(HE)staining to systematically evaluate the anti-atherosclerotic effects of Tiaozhi Xiaoban Mixture at different doses.Biochemical kits were utilized to assess relevant indices related to blood lipid levels as well as liver and kidney function,thereby evaluating the impact of Tiaozhi Xiaoban Mixture on these parameters.Enzyme-linked immunosorbent assay(ELISA)was employed to measure serum inflam-mation markers influenced by Tiaozhi Xiaoban Mixture.Additionally,TUNEL staining and Western blot analysis were conducted to ex-amine the apoptotic effects of Tiaozhi Xiaoban Mixture on thoracic aorta tissue.Finally,qPCR was used to detect the expression levels of Line-HC,MALAT1,etc.,in order to evaluate how Tiaozhi Xiaoban Mixture affecting these specific RNA molecules.RESULTS Following treatment with Tiaozhi Xiaoban Mixture,the blood lipid profiles indicated that total cholesterol(TC),triglycerides(TG),and low-density lipoprotein cholesterol(LDL-C)were significantly down-regulated(P＜0.05,P＜0.01),while high-density lipopro-tein cholesterol(HDL-C)levels were up-regulated in the atherosclerotic rats.Moreover,serum levels of liver and kidney function markers such as aspartate aminotransferase(AST),alanine aminotransferase(ALT),blood urea nitrogen(BUN),and creatinine(Cr)exhibited down-regulation(P＜0.05,P＜0.01).Additionally,pro-inflammatory factors including interleukin-6(IL-6),interleukin-1 beta(IL-1β),tumor necrosis factor-alpha(TNF-α),high-sensitivity C-reactive protein(hs-CRP),and matrix metallopeptidase 9(MMP-9)were also reduced(P＜0.01),whereas the anti-inflammatory factor interleukin-10(IL-10)was found to be elevated(P＜0.01).Furthermore,after oral administration of Tiaozhi Xiaoban Mixture,expression levels of apoptosis-related factors NLRP3,ASC,Cleaved Caspase-1,Cleaved IL-1 β,Puma,Bax,Noxa,and MDM2 in thoracic aorta tissues from the atherosclerotic rats showed sig-nificant down-regulation(P＜0.05,P＜0.01).Notably,following treatment with Tiaozhi Xiaoban Mixture,mRNA levels of Linc-HC decreased while mRNA expression of MALAT1 increased(P＜0.05,P＜0.01).CONCLUSION Tiaozhi Xiaoban Mixture may inhibit the expression of Linc-HC and up-regulate the expression of MALAT1 to reduce the formation of atherosclerotic plaque,improve ab-normal blood lipids and liver and kidney function,alleviate inflammation and inhibit apoptosis.

Objective To evaluate the clinical efficacy of Long's spinal manipulation therapy combined with four postures of rotational tendon-releasing maneuver in treating lumbar disc herniation(LDH).Methods Sixty-eight patients diagnosed with LDH were enrolled from the Department of Acupuncture and Rehabilitation at Guangdong Second Traditional Chinese Medicine Hospital between July 2024 and December 2024.Patients were randomly divided into an observation group and a control group using a random number table,with 34 cases in each group.The control group received Long's spinal manipulation therapy,while the observation group received additional training in four postures of rotational tendon-releasing maneuver.Both groups were treated for 2 weeks,with a 1-month follow-up.Clinical efficacy was evaluated after treatment by comparing Visual Analogue Scale(VAS)scores,Oswestry Disability Index(ODI)scores,and Japanese Orthopaedic Association(JOA)scores before and after intervention.Spinal parameters including maximum lumbar curvature angle,pelvic tilt angle,and maximum vertebral rotation angle were assessed using the DIERS formetric 3D evaluation system.Results(1)The total effective rate was 94.12%(32/34)in the observation group and 91.18%(31/34)in the control group,and both groups achieved ideal therapeutic efficacy,but the difference was not statistically significant(P＞0.05).(2)After treatment,the VAS scores of patients in the two groups significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving VAS scores,with statistically significant differences(P＜0.05).At the 1-month follow-up after treatment,the VAS score of the observation group significantly improved(P＜0.05),and the observation group was significantly superior to the control group,and the difference was statistically significant(P＜0.05).(3)After treatment,the JOA and ODI scores of the patients in the two groups significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the JOA and ODI scores,and the difference was statistically significant(P＜0.05).At 1-month follow-up after treatment,JOA and ODI scores of the observation group significantly improved(P＜0.05),and the observation group was significantly superior to the control group,with statistically significant differences(P＜0.05).(4)After treatment,the maximum angle of lumbar flexion,pelvic torsion angle,and maximum rotation angle of the vertebral body of the two groups of patients were significantly improved(P＜0.05),and the observation group was significantly better than the control group,and the difference was statistically significant(P＜0.05).At the 1-month follow-up after treatment,the maximum angle of lumbar flexion,pelvic torsion angle,and maximum vertebral rotation angle of the observation group were significantly improved(P＜0.05),and the observation group was significantly superior to the control group,and the difference was statistically significant(P＜0.05).Conclusion The combination of Long's spinal manipulation therapy and four postures of rotational tendon-releasing maneuve significantly improves spinal alignment,reduces pain and disability,and enhances quality of life in patients with LDH,demonstrating notable clinical efficacy.

OBJECTIVE To explore the ameliorative effect of Tiaozhi Xiaoban Mixture on atherosclerosis and the potential role of long non-coding RNA(Linc RNA)in anti-atherosclerosis.METHODS A model of atherosclerosis was established in SD rats subjec-ted to a high-fat diet.At 4 weeks post-modeling,thoracic aortic tissues from atherosclerotic rats were collected for hematoxylin-eosin(HE)staining to systematically evaluate the anti-atherosclerotic effects of Tiaozhi Xiaoban Mixture at different doses.Biochemical kits were utilized to assess relevant indices related to blood lipid levels as well as liver and kidney function,thereby evaluating the impact of Tiaozhi Xiaoban Mixture on these parameters.Enzyme-linked immunosorbent assay(ELISA)was employed to measure serum inflam-mation markers influenced by Tiaozhi Xiaoban Mixture.Additionally,TUNEL staining and Western blot analysis were conducted to ex-amine the apoptotic effects of Tiaozhi Xiaoban Mixture on thoracic aorta tissue.Finally,qPCR was used to detect the expression levels of Line-HC,MALAT1,etc.,in order to evaluate how Tiaozhi Xiaoban Mixture affecting these specific RNA molecules.RESULTS Following treatment with Tiaozhi Xiaoban Mixture,the blood lipid profiles indicated that total cholesterol(TC),triglycerides(TG),and low-density lipoprotein cholesterol(LDL-C)were significantly down-regulated(P＜0.05,P＜0.01),while high-density lipopro-tein cholesterol(HDL-C)levels were up-regulated in the atherosclerotic rats.Moreover,serum levels of liver and kidney function markers such as aspartate aminotransferase(AST),alanine aminotransferase(ALT),blood urea nitrogen(BUN),and creatinine(Cr)exhibited down-regulation(P＜0.05,P＜0.01).Additionally,pro-inflammatory factors including interleukin-6(IL-6),interleukin-1 beta(IL-1β),tumor necrosis factor-alpha(TNF-α),high-sensitivity C-reactive protein(hs-CRP),and matrix metallopeptidase 9(MMP-9)were also reduced(P＜0.01),whereas the anti-inflammatory factor interleukin-10(IL-10)was found to be elevated(P＜0.01).Furthermore,after oral administration of Tiaozhi Xiaoban Mixture,expression levels of apoptosis-related factors NLRP3,ASC,Cleaved Caspase-1,Cleaved IL-1 β,Puma,Bax,Noxa,and MDM2 in thoracic aorta tissues from the atherosclerotic rats showed sig-nificant down-regulation(P＜0.05,P＜0.01).Notably,following treatment with Tiaozhi Xiaoban Mixture,mRNA levels of Linc-HC decreased while mRNA expression of MALAT1 increased(P＜0.05,P＜0.01).CONCLUSION Tiaozhi Xiaoban Mixture may inhibit the expression of Linc-HC and up-regulate the expression of MALAT1 to reduce the formation of atherosclerotic plaque,improve ab-normal blood lipids and liver and kidney function,alleviate inflammation and inhibit apoptosis.

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.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*