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 establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

Objective:To investigate the nutritional risk status of children in PICU and analyze its influencing factors.Methods:From July 2021 to February 2023,all children aged 1 to 18 years admitted to PICU of Beijing Children's Hospital were investigated by using the pediatric Yorkhill Malnutrition Scoring tool(PYMS)and the clinical data questionnaire.Results:A total of 492 children in PICU were enrolled.The first nutritional risk screening results showed that there were 32 cases of no/low nutritional risk(6.5%),76 cases of medium risk(15.4%),and 384 cases of high risk(78.1%).The incidence of medium/high nutritional risk was as high as 93.5%.The PYMS score of nutritional risk in PICU was(2.61±1.42).The results of multiple linear regression analysis showed that weight,fever time before admission,white blood cells,body mass index,primary diagnosis,father's education,and diet before illness were the main influencing factors of nutritional risk of children in PICU(P<0.05).Conclusion:Children in PICU are in a state of high nutritional risk.It is suggested that children in PICU should carry out nutritional screening in a standardized manner,identify children with high nutritional risk and its influencing factors early.To actively conduct nutritional assessment and nutritional intervention could improve the clinical outcome of children in PICU.

Objective To investigate the correlation of monocyte count(MONO)to high density lipopro-tein-cholesterol(HDL-C)ratio(MHR)and systemic immune-inflammation index(SII)with diabetic retinop-athy(DR).Methods Patients with type 2 diabetes mellitus(T2DM)admitted to the hospital from June 2020 to May 2023 were selected as the research objects.According to the presence or absence of DR,the patients were divided into non-retinopathy group(NDR group)and DR Group.The differences in basic information,blood routine,and biochemical indexes between the two groups were analyzed,and the MHR and SII were cal-culated.Multivariate Logistic regression was used to analyze the risk factors for DR.Spearman correlation a-nalysis was used to analyze the correlation between risk factors and DR.The receiver operating characteristic(ROC)curve was used to evaluate the value of MHR and SII in predicting DR in T2DM patients.Results A total of 291 T2DM patients were enrolled,including 135 patients in the NDR group and 156 patients in the DR group.Compared with the NDR group,duration of diabetes was significantly prolonged(P<0.05),glycosy-lated hemoglobin(HbA1c),creatinine,fasting plasma glucose(FPG),total cholesterol(TC),platelet count(PLT),MHR and SII were increased(P<0.05),and high density lipoprotein-cholesterol(HDL-C)was de-creased(P<0.05)in the DR Group.Spearman correlation analysis showed that DR was positively correlated with duration of diabetes,FPG,HbA1c,PLT,MHR and SII(P<0.05),and negatively correlated with HDL-C(P<0.05).Multivariate Logistic regression analysis showed that gender(OR=0.151,95%CI 0.052-0.432,P<0.001),history of heavy drinking(OR=7.199,95%CI 2.845-18.216,P<0.001),duration of di-abetes(OR=1.570,95%CI 1.354-1.821,P<0.001),HbA1c(OR=1.218,95%CI 1.013-1.464,P=0.036),MHR(OR=1.054,95%CI 1.028-1.080,P<0.001)and SII(OR=1.002,95%CI 1.001-1.003,P=0.002)were independent influencing factors for DR patients.ROC curve analysis showed that the area un-der the curve(AUC)of MHR and SII in predicting the development of T2DM to DR was 0.696 and 0.567,re-spectively.The AUC of MHR and SII combined in predicting DR was 0.702.Conclusion MHR and SII are closely related to the incidence of DR,and both have certain predictive value for DR,and the predictive value of the combined of MHR and SII is higher.

Objective:To investigate the association of monocyte-to-high-density lipoprotein cholesterol ratio (MHR) with the severity of white matter hyperintensities (WMHs) and its spatial distribution.Methods:Patients admitted to the Department of Neurology, Jiangning Hospital Affiliated to Nanjing Medical University due to various chronic diseases or physical examinations between January 2023 and December 2024 were included retrospectively. Past medical history, clinical and imaging data were collected. The Fazekas scale was used to assess the severity of WMHs. According to the scoring results of periventricular WMHs (PVWMHs) and deep WMHs (DWMHs), WMHs were divided into no/mild group (0-1 points) and moderate/severe group (2-3 points). Multivariate logistic regression analysis was used to determine independent correlation factors for the severity of WMHs, PVWMHs, and DWMHs. Results:A total of 357 patients were included, aged 65.42±9.95 years, with 198 males (55.5%). There were 193 patients (54.1%) in the no/mild group and 164 (45.9%) in the moderate/severe group. Univariate analysis showed that the proportion of patients with hypertension, diabetes, history of cerebral infarction and cerebral hemorrhage, carotid plaque, and age, serum creatinine, monocyte count and MHR in the moderate/severe group were significantly higher than those in the no/mild group (all P<0.05). Multivariate logistic regression analysis showed a significant positive correlation between MHR and the severity of WMHs (odds ratio 3.138, 95% confidence interval 1.042-9.451; P=0.042). Further analysis showed a significant positive correlation between MHR and PVWMHs (odds ratio 3.384, 95% confidence interval 1.111-10.305; P=0.032), but no independent correlation with DWMHs. In addition, age and hypertension, diabetes, history of cerebral infarction and cerebral hemorrhage were significantly positively correlated with the severity of WMHs, PVWMHs and DWMHs. Conclusion:MHR is correlated with the severity of WMHs, and higher MHR is significantly associated with PVWMHs, but not with DWMHs.

Objective:To investigate the correlation between body mass index (BMI)/high-density lipoprotein cholesterol (HDL-C) ratio and cerebral small vessel disease (CSVD) in middle-aged and elderly people.Methods:Consecutive middle-aged and elderly patients (aged ≥40 years) who were hospitalized for chronic disease examinations in the Department of Neurology, Jiangning Hospital Affiliated to Nanjing Medical University between February 2022 and May 2024 were included prospectively. According to the overall burden score of CSVD, they were divided into CSVD group (≥1) and non-CSVD group (0). According to age, they divided into middle-aged group (40-59 years old) and elderly group (≥60 years old). The demographic characteristics and clinical data were collected. Binary multivariate logistic regression analysis was used to determine the independent correlation between BMI/HDL-C ratio and CSVD. Forest plot was used to analyze the correlation between BMI/HDL-C ratio and CSVD in different age groups. Results:A total of 710 patients were included, with an age of 66.0±10.0 years and 361 were males (50.8%). There were 261 patients (36.8%) in the CSVD group and 449 (63.2%) in the non-CSVD group. The BMI/HDL-C ratio in the CSVD group was significantly higher than that in the non-CSVD group (23.60±7.00 vs. 20.78±6.40; P<0.001). Multivariate logistic regression analysis showed that BMI/HDL-C ratio was an independent risk factor for CSVD in middle-aged and elderly populations (odds ratio 1.046, 95% confidence interval 1.027-1.064; P<0.001). There were 475 patients in the elderly group, of which 198 (41.7%) had CSVD; there were 235 patients in the middle-aged group, of which 63 (26.8%) had CSVD. Forest plot analysis showed that the association between BMI/HDL-C ratio and CSVD still had statistical significance in different age groups, but the effect intensity was higher in the elderly group than in the middle-aged group. Conclusion:The BMI/HDL-C ratio is independently correlated with CSVD in middle-aged and elderly population, particularly significant in the elderly population.

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.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets