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 risk factors of hemodynamic instability after carotid artery stenting by meta-analysis.Methods Ten databases were searched:PubMed,ProQuest,ScienceDirect,Embase,Cochrane Library,Web of Science,China Knowledge Network,Wanfang Data,VIP Information Database,and China Biomedical Database.The search date was from inception until 2 February 2024,and meta-analysis was performed using Stata 16.0 statistical software.Results A total of 27 studies with 4199 subjects and 22 influencing factors were included.The studies showed a 37.4％(95％CI 30.3％-44.8％)incidence of haemodynamic instability after carotid stenting,Meta-analysis determined that age＞60 years(P＜0.001),hypertension(P＜0.001),calcified plaque(P＜0.001),stenosis＞70％(P=0.008),eccentric plaque(P=0.002),distance from the largest stenosis to the carotid bifurcation≤ 10 mm(P＜0.001),stenosis involvement of the balloon or bifurcation(P＜0.001),balloon post-dilation(P=0.003),open-loop stenting(P＜0.001),dilated balloon diameter≥5 mm(P=0.002),repeat balloon dilation(P=0.011)and balloon dilation pressure≥8 atm(P＜0.001)are risk factors for intraoperative and postoperative haemodynamic instability in patients undergoing carotid artery stenting surgery.Statin use was a protective factor(P＜0.001).Conclusions Medical staff working in the clinic should assess the patient's condition preoperatively,identify risk factors that may lead to haemodynamic instability,and avoid unnecessary intraoperative stimulation of patients who are already in a high-risk state.Reduce postoperative clinical complications in patients with carotid artery stenosis and improve patient recovery.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

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.

To investigate the phenotype and genomic characterization of a mucoid-type Salmonella Saintpaul ST50 isolate from a urinary tract infection patient,promoting clinical diagnosis and treatment for urinary tract infections caused by Salmo-nella spp.Culture-based quantitative counts of midstream urine sample from the patient were conducted,and further biochemi-cal identification,mass spectrometry detection,serum agglutination test and antimicrobial susceptibility test(AST)were con-ducted on Salmonella isolate(2024JD5).Whole-genome sequencing(WGS)was performed on isolate 2024JD5 to predict sero-type,multilocus sequence type(MLST),resistance genes,and virulence genes.Two smooth-type of Salmonella Saintpaul ST50 were selected as comparative genomic reference strains from the Chinese local Salmonella genome database.The literature reviews of global Salmonella serotype of urinary tract infection were summarized.Specific serum agglutination confir-mation of isolate 2024JD5 failed due to characterization of the mucus type.The strain 2024JD5 was predicted as Salmonella Saintpaul(4,5,12:e,h:1,2)ST50 using WGS,and was resistant to ciprofloxacin,nalidixic acid,chloramphenicol and tetracy-cline with carrying aminoglycoside resistance genes aac(6')-Ⅰaa and aph(3)-Ⅱa,chloramphenicol resistance gene floR,tetra-cycline resistance gene tet,quinolone resistance gene qnrS1,and S83Y substitution in the gyrA gene was found in the quinolo-ne resistance determination region(QRDR).In addition,the strain 2024JD4 carried six types of non-plasmid-based mobile ge-netic elements and 144 virulence genes,including 71 secretion transporter genes and 58 fimbriae adhesion genes,respectively.Four types of fimbriae regulatory genes(csgB,csgC,fimW,fimY)were absent in comparison with smooth-type Salmonella Saintpaul.The literature reviews showed Salmonella Saintpaul was currently a rare Salmonella serotype in cases of urinary tract infections worldwide.Salmonella Saintpaul ST50 with mucoid-type is the pathogen of urinary tract infection with multi-drug resistant phenotypic and genotypic characteristics,and the high mucoid expression may be related to the compensatory mechanism of fimbriae regulatory genes absence in urinary tract colonization and adaptation.WGS combined with the Chinese local Salmonella genome database can effectively solve the diagnosis and biosafety assessments of rare Salmonella phenotypes.

As the significance of the doctor-patient relationship has become increasingly prominent,seeking effective communication methods to enhance the quality of interaction between the two sides has become an urgent need.This paper aimed to explore the application of multimodal communication and its role in promoting the doctor-patient relationship,as well as to analyze and improve the comprehensiveness and effectiveness of doctor-patient communication by integrating various communication modes such as language,non-verbal behavior,visual aids,and digital tools.Through interviews and investigations conducted at three tertiary A hospitals in M city,it was found that multimodal communication can significantly enhance the clarity of information transmission and patients'understanding,promote emotional expression,and encourage patients to actively participate in the treatment process,thereby effectively improving the harmony of doctor-patient interaction.Multimodal communication also played a crucial role in strengthening the doctor-patient relationship,mainly in terms of being conducive to building a mutually trusting doctor-patient relationship,facilitating various communication strategies for empathy,enhancing multi-channel information exchange approaches,mediating crises and conflicts in the doctor-patient relationship,and other aspects.Therefore,this paper proposed strategies for enhancing a harmonious doctor-patient relationship through multimodal communication.These strategies included building multimodal bridges for differential adaptation and improving the practical mechanism of doctor-patient communication strategies;training doctors'multimodal communication skills and enhancing the effective transmission of emotion and information;implementing a multimodal evaluation and feedback mechanism and establishing an immediate and long-term evaluation system;and motivating patients to participate in multimodal strategies to enhance their initiative in medical interactions.Future medical practice and policymaking should attach greater importance to the systematic training and implementation of multimodal communication strategies to fully realize their potential value in the doctor-patient relationship.