BACKGROUND:The number of patients receiving total knee arthroplasty has been increasing globally each year.Pain management is a crucial aspect following total knee arthroplasty,as effective pain control can facilitate early mobilization,reduce complications,enhance patient satisfaction,and accelerate the rehabilitation process.OBJECTIVE:To construct a visual map of post-total knee arthroplasty pain,understand the international research status and trends in this field,and provide a reference for future studies.METHODS:Relevant research articles on post-total knee arthroplasty pain were retrieved from the CNKI,WanFang Data,and Web of Science core databases,covering the period from January 2000 to December 2023.The CiteSpace software(version 6.2.3)was used to analyze the annual publication output,authors,institutions,countries,keywords,and references.Utilizing R programming language(version 4.4.1),a database was established to create line charts and bar graphs.RESULTS AND CONCLUSION:(1)Our analysis included 3 796 publications,predominantly in Chinese(3 509 articles)with the remainder in English(287 articles).(2)The United States was the most productive country in English literature,with Harvard University leading institutional output.Guangzhou University of Chinese Medicine was the top publishing institution in Chinese literature.(3)Keyword clustering identified"quality of life,""phobia,"and"acupuncture"as emerging focal points in Chinese literature,while"satisfaction"and"psychological factors"were prominent in English literature over the past five years.Co-occurrence and clustering analysis revealed dense internal connections among institutions,authors,and publications,but sparse external collaborations.(4)The study's bias on visualization analysis may have introduced bias by excluding less influential papers.(5)Regarding research hotspots,domestic research emphasized the efficacy and exploration of analgesic methods,in contrast to international research that focused on pain mechanism subtyping and analgesic drug innovation.Future research is expected to trend towards traditional Chinese medicine for postoperative pain,multimodal analgesia,and the investigation and prevention of pain typing mechanisms.

Onco-critical care has emerged as an important subspecialty at the intersection of critical care medicine and oncology, attracting increasing attention in recent years. With continuous innovations in cancer therapies, patient survival has improved significantly; however, the incidence of associated critical complications has also increased. The reasons for cancer patients requiring intensive care unit admission are diverse and can be broadly categorized into three groups: progression of the underlying malignancy, treatment-related complications, and coexisting classical critical illnesses. Traditional critical care concepts and practices face limitations in addressing the multidimensional and heterogeneous challenges of onco-critical care. Based on the core mechanism of critical illness development—host/organ unregulated response (HOUR)—this article systematically elaborates on how this framework advances understanding and clinical practice into onco-critical care, with emphasis on its manifestations in neuroendocrine, immune-inflammatory, and coagulation-metabolic pathways. The review summarizes recent advances in clinical assessment and phenotyping systems for onco-critical illness and discusses a multidisciplinary, integrated management strategy centered on the "Disease Control, Host Response Modulation, Organ Support" triad. Finally, major challenges and future directions in this field are outlined. By integrating existing evidence and theoretical insights, this review aims to provide new perspectives and a theoretical foundation for the clinical management of onco-critical illness, thereby promoting its evolution toward precision and standardization.

With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

With the rising incidence of diabetes， diabetic kidney disease （DKD） has become a significant global health burden. Although current prevention and treatment strategies can partially delay the progression of DKD， the risk of patients advancing to end-stage renal disease remains high. Since the concept of the "gut-kidney axis" was first introduced at the International Congress on Dialysis in 2011， research on the role of gut microbiota in the pathogenesis of DKD has received increasing attention. This review summarizes the current research on gut microbiota， explores the mechanisms through which it contributes to DKD development， and outlines clinical approaches for DKD prevention and treatment based on the "gut-kidney axis" theory. Evidence indicates that dietary interventions， intake of probiotics or prebiotics， use of metformin and novel antidiabetic drugs， and application of traditional Chinese medicine （TCM） compound formulas can effectively improve gut microbiota composition， influence metabolite production， and restore the intestinal mucosal barrier. These interventions can further regulate intestinal innate immunity and inflammatory responses， thereby modulating the progression of DKD. Despite challenges posed by the traditional oral administration of water-decocted TCM compound formulas and the complexity of their ingredients， increasing evidence suggests that TCM may indirectly affect the occurrence and development of DKD by modulating gut microbiota. This finding provides a new perspective on the potential mechanisms of TCM in DKD treatment and may offer novel strategies for DKD prevention and therapy.

ObjectiveTo evaluate the public health governance capacity in Jiangsu Province and provide an optimized pathway for the construction of a “strong, rich, beautiful, and high-quality” new Jiangsu. MethodsA total of 806 policy documents, 658 public information reports, and 148 research literatures related to public health governance capacity in Jiangsu Province from January 1995 to December 2023 were collected. The status of current public health goverance was assessed based on the evaluation criteria suitable for public health systems, and the strengths and the weaknesses of the system were identified. ResultsThe public health governance capability of Jiangsu Province was scored at 738.3 points, ranking 3rd nationally. Maternal health care and emergency response capacities achieved leading positions nationwide, both ranking 2nd. Jiangsu had exhibited a standardized guidance in the strategic level, a well-established management mechanism, an extensive coverage in information collection, and a scientifically established health targets setting. However, bottlenecks remained, including an unclear division of responsibilities across organizational departments, an insufficient public-health workforce, the absence of a stable growth mechanism for government funding investment, and difficulties in promptly identifying public needs. ConclusionJiangsu’s public-health system demonstrates leading nationally, yet several components remain underdeveloped. Future efforts should consolidate advantages while addressing weaknesses, further diversify content and forms, establish a stable funding increase mechanism, and clarify departmental functions, thereby providing solid health support for realizing the developmental goals of a “strong, rich, beautiful and high-quality” new Jiangsu.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

BACKGROUND:The number of patients receiving total knee arthroplasty has been increasing globally each year.Pain management is a crucial aspect following total knee arthroplasty,as effective pain control can facilitate early mobilization,reduce complications,enhance patient satisfaction,and accelerate the rehabilitation process.OBJECTIVE:To construct a visual map of post-total knee arthroplasty pain,understand the international research status and trends in this field,and provide a reference for future studies.METHODS:Relevant research articles on post-total knee arthroplasty pain were retrieved from the CNKI,WanFang Data,and Web of Science core databases,covering the period from January 2000 to December 2023.The CiteSpace software(version 6.2.3)was used to analyze the annual publication output,authors,institutions,countries,keywords,and references.Utilizing R programming language(version 4.4.1),a database was established to create line charts and bar graphs.RESULTS AND CONCLUSION:(1)Our analysis included 3 796 publications,predominantly in Chinese(3 509 articles)with the remainder in English(287 articles).(2)The United States was the most productive country in English literature,with Harvard University leading institutional output.Guangzhou University of Chinese Medicine was the top publishing institution in Chinese literature.(3)Keyword clustering identified"quality of life,""phobia,"and"acupuncture"as emerging focal points in Chinese literature,while"satisfaction"and"psychological factors"were prominent in English literature over the past five years.Co-occurrence and clustering analysis revealed dense internal connections among institutions,authors,and publications,but sparse external collaborations.(4)The study's bias on visualization analysis may have introduced bias by excluding less influential papers.(5)Regarding research hotspots,domestic research emphasized the efficacy and exploration of analgesic methods,in contrast to international research that focused on pain mechanism subtyping and analgesic drug innovation.Future research is expected to trend towards traditional Chinese medicine for postoperative pain,multimodal analgesia,and the investigation and prevention of pain typing mechanisms.

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.

Gastric ulcer （GU） is a high-incidence digestive system disease characterized pathologically by disruption of gastric mucosal integrity， with clinical features including a prolonged course and periodic recurrence. Modern medicine attributes its pathogenesis to the dynamic imbalance between aggressive and defensive factors，while traditional Chinese medicine （TCM） posits its development as closely linked to spleen deficiency. Current therapies combining acid suppressants and antibiotics face challenges such as high recurrence rates，poor mucosal healing，and adverse drug reactions. Long-term use may induce metabolic disturbances like hypergastrinemia and reduced intestinal microbiota diversity. Therefore，exploring safer and longer-lasting therapeutic strategies has become a critical focus. TCM has extensive clinical experience and unique advantages in GU prevention and treatment. Studies demonstrate that the classic formula Shenling Baizhu San exhibits therapeutic properties of "invigorating spleen and tonifying Qi to restore physiological balance and eliminating dampness and regulating middle energizer to unblock Qi movement"， enabling a holistic approach targeting both symptoms and root causes in GU with spleen deficiency as the core pathology by suppressing aggressive factors and strengthening defensive factors. Experimental research reveals its mechanisms involve enhancing the physicochemical barrier of the mucus layer，repairing epithelial barriers and microcirculation，modulating gastric acid secretion and gastrointestinal motility，and regulating microecological barriers and mucosal immunity. Clinical evidence confirms its synergistic effects in promoting ulcer healing，improving Helicobacter pylori eradication rates，and reducing recurrence risks. This review examined the etiology and pathogenesis of GU and systematically evaluated Shenling Baizhu San from three perspectives-clinical application，pharmacological effects， and experimental research-to provide insights for optimizing integrated traditional Chinese and Western medicine protocols and expanding its clinical applications.