BACKGROUND:The surgical treatment of severe lumbar fractures is mainly based on posterior short-segment fixation,and different pedicle screw fixation methods have differences in biomechanical properties.OBJECTIVE:To simulate three different screw fixation techniques in the treatment of severe L1 vertebral fractures using three-dimensional finite element technology,and to compare and analyze the biomechanical characteristics of the upper and lower intervertebral discs,fractured vertebrae,and internal fixation devices during the simulated spinal movement.METHODS:A three-dimensional model of the T11-L3 spine was established to simulate severely unstable fracture of the lumbar 1 vertebral body using the designed three pedicle screw fixation methods:Model A with 4 traditional pedicle screws(2 in each T12 and L2),model B with 4 Universal Spine System pedicle screws(2 in each T12 and L2),and Model C were bilateral intermediate fixation with 6 pedicle screws(2 in each of T12,L1,and L2).Restraint and load were applied to simulate the maximum stress and maximum displacement of the internal fixation,the displacement of the L1 vertebral body,and stress on the intervertebral discs superior and inferior to the injured vertebra during the anterior flexion,posterior extension,left-sided bending,right-sided bending and left-right rotation of the lumbar spine.RESULTS AND CONCLUSION:(1)The maximum displacement and motion range of the lumbar 1 vertebra with Model C in flexion and extension,left bending,right bending,left rotation,and right rotation were lowest in the tested motion states;the maximum stress of the screws and connecting rods was the smallest;and the maximum stress of intervertebral discs superior and inferior to the injured vertebra was the smallest.This was followed by the Model B,while the maximum displacement and motion range of the lumbar 1 vertebra and the stress and displacement of the screw connecting rod in the Model A were highest,but it showed the worst biomechanical stability and was more prone to adjacent segment degeneration.(2)These results show that bilateral intermediate fixation with six pedicle screws during the treatment of severe lumbar fractures is more dispersed in internal fixator stress and exhibits the least displacement and motion range of the vertebral pedicle screws and rods compared with four universal spinal system pedicle screws and four traditional pedicle screws.Thus,it can effectively reduce the occurrence of internal fixation failure and the adjacent segment degeneration.

BACKGROUND:The surgical treatment of severe lumbar fractures is mainly based on posterior short-segment fixation,and different pedicle screw fixation methods have differences in biomechanical properties.OBJECTIVE:To simulate three different screw fixation techniques in the treatment of severe L1 vertebral fractures using three-dimensional finite element technology,and to compare and analyze the biomechanical characteristics of the upper and lower intervertebral discs,fractured vertebrae,and internal fixation devices during the simulated spinal movement.METHODS:A three-dimensional model of the T11-L3 spine was established to simulate severely unstable fracture of the lumbar 1 vertebral body using the designed three pedicle screw fixation methods:Model A with 4 traditional pedicle screws(2 in each T12 and L2),model B with 4 Universal Spine System pedicle screws(2 in each T12 and L2),and Model C were bilateral intermediate fixation with 6 pedicle screws(2 in each of T12,L1,and L2).Restraint and load were applied to simulate the maximum stress and maximum displacement of the internal fixation,the displacement of the L1 vertebral body,and stress on the intervertebral discs superior and inferior to the injured vertebra during the anterior flexion,posterior extension,left-sided bending,right-sided bending and left-right rotation of the lumbar spine.RESULTS AND CONCLUSION:(1)The maximum displacement and motion range of the lumbar 1 vertebra with Model C in flexion and extension,left bending,right bending,left rotation,and right rotation were lowest in the tested motion states;the maximum stress of the screws and connecting rods was the smallest;and the maximum stress of intervertebral discs superior and inferior to the injured vertebra was the smallest.This was followed by the Model B,while the maximum displacement and motion range of the lumbar 1 vertebra and the stress and displacement of the screw connecting rod in the Model A were highest,but it showed the worst biomechanical stability and was more prone to adjacent segment degeneration.(2)These results show that bilateral intermediate fixation with six pedicle screws during the treatment of severe lumbar fractures is more dispersed in internal fixator stress and exhibits the least displacement and motion range of the vertebral pedicle screws and rods compared with four universal spinal system pedicle screws and four traditional pedicle screws.Thus,it can effectively reduce the occurrence of internal fixation failure and the adjacent segment degeneration.

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.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

Objective To introduce the causes of accidents and the diagnosis and treatment of two patients with radiation-induced skin injury admitted to our hospital in 2023, and to provide a reference for the clinical treatment of subsequent radiation-induced skin injury. Methods The clinical treatment process of two patients with acute skin injury caused by external radiation exposure were summarized and analyzed. Results The exposure history of the two patients was reconstructed, the flaw detection scenario was simulated, the biological dose and hand skin exposure dose were estimated, and the infrared thermal imaging device was used for dynamic monitoring. A comprehensive analysis was conducted based on clinical manifestations and other data. The diagnosis of “Xie” was excessive exposure combined with acute radiation-induced skin injury on both hands (Grade IV for the right hand palm, index finger, and middle finger and Grade II for the left hand little finger). The diagnosis of “Hao” was acute radiation-induced skin injury on both hands (Grade I). The two patients received different clinical treatment measures: “Xie” was treated with both local and systemic therapies, while “Hao” was mainly treated with systemic therapy. Conclusion After systematic and effective treatment, the radiation-induced skin injuries healed in both patients.

Driven by policy initiatives promoting the integration of digital-intelligent technologies with primary healthcare and the digital transformation of traditional Chinese medicine (TCM), Smart TCM has emerged as a pivotal strategy for enhancing primary healthcare services for major chronic diseases. This paper reviews the current status, challenges, and feasible pathways of Smart TCM in community-level management of cardiovascular–cerebrovascular diseases and tumors, which represent major chronic disease burdens. Our findings indicate that Smart TCM demonstrates emerging potential in primary healthcare for chronic diseases across the entire continuum of “prevention-screening-diagnosis-treatment-management.” However, several significant challenges persist, including data silos and security vulnerabilities, limited applicability of existing models to real-world clinical needs, and insufficient digital literacy among primary healthcare physicians and elderly patients. To address these constraints, this paper proposes a multidimensional strategy encompassing the development of secure and interoperable regional data platforms, lightweight intelligent devices and support services aligned with primary care capacity, unified technical and data standards with corresponding quality control systems, adaptive and dynamically updated artificial intelligent models, interdisciplinary workforce training and patient education programs, and enhanced policy and health insurance support. Overall, Smart TCM shows great promise for improving the efficiency of primary healthcare delivery and establishing innovative TCM-based chronic disease management paradigms.

Objective:To investigate the clinical characteristics of patients with multiple myeloma(MM)complicated by bone lesions and the risk factors associated with bone lesions.Methods:The clinical data of 294 newly diagnosed MM patients in Gansu Provincial Hospital from January 2017 to June 2021 were retrospectively analyzed.The patients were divided into the bone lesion group(154 cases)and the non-bone lesions group(140 cases)based on the presence of absence of bone lesions at diagnosis.The general data and laboratory parameters were compared between the two groups.The risk factors for bone lesions in MM patients were analyzed by logistic regression analysis,and the characteristic(ROC)curves were plotted to assess the predictive value of each risk factor for the occurrence of bone lesions in MM patients.Results:Compared to the non-bone lesion group,the bone lesion group had significantly higher serum calcium levels and significantly greater proportions of patients with Durie-Salmon(DS)stage Ⅲ,and bone pain(all P＜0.05).Logistic regression analysis showed that elevated serum calcium(OR=5.135,95％CI:1.931-13.653,P=0.001),DS stage Ⅲ(OR=1.841,95％CI:1.019-3.328,P=0.043),and bone pain(OR=8.208,95％CI:4.761-14.151,P＜0.001)were independent risk factors for bone lesions in MM patients.ROC curve analysis showed that serum calcium(AUC=0.619,95％CI:0.555-0.683,P＜0.001)and bone pain(AUC=0.743,95％CI:0.692-0.793,P＜0.001)had predictive value for bone lesions in MM patients.Conclusion:MM patients have a high incidence of bone lesions,and active monitoring and management of risk factors may improve treatment outcomes and prognosis.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

OBJECTIVE To establish and verify the risk prediction models for postoperative multidrug-resistant or-ganisms(MDROs)infections in the liver transplantation patients based on the machine learning algorithms so as to provide bases for identification of the population at high risk of postoperative MDROs infections.METHODS The liver transplantation patients who were retrospectively collected from intensive care Ⅳ database(MIMIC-Ⅳ)and eICU collaborative research database(eICU)were recruited as the research subjects,meanwhile,the patients who underwent liver transplantation in the First Hospital of Shanxi Medical University from Jan.2021 to Jul.2024 were assigned as the external verification group.The variables were selected by Lasso regression,and the models were established based on 7 types of machine learning algorithms such as extreme gradient boosting algorithm and random forest.The predictive performances of the models were evaluated by comparing the areas under receiver operating characteristic(ROC)curves and the accuracy,the characteristic variables were interpreted by Shapley additive explanations(SHAP),and the risk prediction calculator was established.RESULTS A total of 637 pa-tients were finally enrolled in the study,and the incidence of postoperative MDROs infections was 35.79％.Total-ly 15 variables were finally selected for construction of the model.The area under the receiver operating character-istic curve of XGBoost model was 0.82 for the internal test set,0.78 for the external test set;the predictive per-formance of XGBoost model was better than that of the rest of 6 models.SHAP algorithm indicated that the top 5 important predictive factors were as follows:hepatic encephalopathy,length of intensive care unit(ICU)stay,albumin,model of end-stage liver disease(MELD)and total length of hospital stay.CONCLUSION The risk pre-diction models that are established based on the machine learning algorithms have remarkable effect on prediction of the postoperative MDROs infections and can accurately identify the liver transplantation patients at high risk of postoperative MDROs infections,which may provide guidance for the identification of high-risk population and the development of prevention and treatment measures for infections.