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: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

We analyzed the epidemiological features and spatial distribution characteristics of human brucellosis in Jining city from 2014 to 2023,to provide a reference for further development of targeted prevention and control strategies and measures.Descrip-tive epidemiological methods were used to analyze the epidemiological characteristics of brucellosis cases in Jining from 2014 to 2023.The spatial regional correlation of brucellosis incidence in Jining and the clustering patterns of local areas were studied through spatial autocorrelation analysis with townships as the basic unit.A total of 3 520 cases of brucellosis were reported in Jining from 2014 to 2023,and the average annual incidence rate was 4.23/100 000,thus indicating a fluctuating trend overall.Reported cases peaked from March to August,and a sex ratio of 2.71 males to 1 female was observed.The 40-59 year age group had the most reported cases(50.39％).The incidence of brucellosis in Jining showed an imbalanced spatial distribution.Brucellosis incidence showed a spatially clustered distribution(Moran's I>0,P<0.05).Hotspots were distributed primarily in Sishui,Qufu,and Zoucheng.A total of one class Ⅰ clustering area and one class Ⅱ clustering area were detected in the spatial and temporal scans,and were located in Sishui,Qufu,and Liangshan county.After pathogenic AMOS-PCR typing analysis,64 Brucella isolates collected from Jinan City from 2022 to 2024 were all of the sheep strain,and sheep biovar 3 was predominant(70.31％).In 2014-2023,although Jining City experienced a high incidence of brucellosis,a downward trend was observed.Brucellosis showed a spatial clustering pattern concentrated in the northeastern region.Therefore,awareness and education must be strengthened among brucellosis practitioners in cluster areas,to en-hance case surveillance,improve the level of protection,and achieve early detection and treatment.

Tuberculosis remains the leading cause of death from a single infectious agent worldwide.China is still one of the countries with a high burden of tuberculosis.The structural and functional damage to lung tissue caused by post-tuberculosis pulmonary disease(PTLD)imposes a considerable disease burden on people.How-ever,due to the insufficient understanding of post-tuberculosis lung diseases in the past,the long-term manage-ment of this disease has been lacking for the post-tuberculosis population.This article elaborates on the epidemiol-ogy and hazards,pathogenesis,clinical manifestations,diagnostic methods,treatment and preventive measures of common PTLD,with the aim of enhancing the awareness and attention of tuberculosis physicians to PTLD.It also summarizes the limitations in the etiological mechanism,diagnosis and treatment methods of PTLD,as well as the urgency of in-depth research.

As the first defense of respiratory system,airway epi-thelial cells(AECs)play an important role in separating the re-spiratory internal and external environment.They are essential for the natural immune function.Small airway lesions are an im-portant early pathology of chronic obstructive pulmonary disease(COPD),when AECs are exposed to harmful particles or gases for a long time,the epithelial barrier is damaged,and the signa-ling pathways which involved in differentiation,repair,and in-flammatory are disordered,resulting in epithelial cell cycle stag-nation and accelerated aging.A number of studies have sugges-ted that AECs of COPD patients express high levels of aging markers,suggesting that senescence of AECs is closely related to COPD.This review discusses the potential mechanisms of AECs senescence in COPD,the impact of AECs senescence on the de-velopment and severity of the disease,and highlights potential targets for modulating cellular senescence in airway epithelium as a therapeutic approach in COPD.

Conducting drug clinical trials can enhance hospitals' clinical research capabilities and influence,making it particularly important to improve the management efficiency of trial projects through multiple approaches.Remote monitoring sys-tems enable clinical researchers to remotely access hospital diagnosis and treatment-related systems online,allowing timely,se-cure,and standardized review of subjects' source data and key data traceability.These systems monitor the progress of drug clini-cal trial projects,control project risks to ensure compliance with laws,regulations,trial protocols,and standard operating proce-dures,and safeguard the rights of subjects.In the internet era,remote monitoring systems complement on-site monitoring,enhan-cing hospitals' risk resilience in drug clinical trials while improving efficiency and reducing costs.This paper analyzes recent do-mestic and international laws,regulations,policy directions,and research progress in remote monitoring for clinical trials,dis-cusses current challenges and shortcomings,and provides recommendations and insights for the future informatization development of remote monitoring systems in hospital drug clinical trials.

Objective:To analyze the current policy design of outpatient benefit policies for patients with hypertension and diabetes(H&D)in urban and rural residents in China,and to provide references for optimizing the outpatient benefit mechanism for patients with H&D.Methods:A questionnaire survey was conducted to collect data on the basic features of General Outpatient Benefit(GOB),Outpatient Medication Guarantee Mechanisms for Hypertension and Diabetes(OMGM-H&D)and Outpatient Benefit for Patient with Chronic and Special Diseases(OB-C&S),focusing on deductibles,policy reimbursement ratios,and maximum payment limits.Descriptive statistical analysis was performed on the data.Results:A total of 334 regions were surveyed,of which 253 regions(75.34％)had implemented all three policies.Regional analysis revealed significant differences(P<0.001)in reimbursement ratios for GOB and the OMGM-H&D between the eastern,central,and western regions,with the western region having notably higher ratios than the eastern and central regions.However,no significant difference (P>0.05) was observed in the reimbursement ratios for OB-C&S across regions. Regarding deductibles,no significant regional difference (P>0.05) was found for GOB,but significant differences (P<0.05) existed for the OMGM-H&D and OB-C&S. Additionally,the annual maximum payment limits for all three policies showed significant regional variations (P<0.001). Conclusions:The outpatient benefits policy for patients with H&D have achieved full coverage nationwide among urban and rural residents in China,but regional equity in benefit levels requires improvement. It is recommended to strengthen data feedback mechanisms and promote provincial-level pooling of medical insurance.

Pancreatic cancer is a highly lethal malignancy,among which pancreatic ductal adenocarcinoma is the most prevalent pathalogical type.The pathogenesis of pancreatic cancer associated with both genetic and non-genetic factors,but its precise etiology remained unclear.Chronic pancreatitis(CP)closely related with pancreatic cancer,which was a significant risk factor of the latter.The research progresses of mechanisms underlying the transformation from CP to pancreatic cancer,imaging assessments,along with relative preventation and treatment strategies were reviewed in this article.