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.

This study investigated the protective effect of arbutin(Arb) in yam on lipopolysaccharide(LPS)-induced acute lung injury(ALI) in a mouse model and revealed its possible mechanism of action by metabolomics technology, providing a theoretical basis for clinical treatment of ALI. SPF BALB/c mice were randomly divided into normal control group, model group, resveratrol(Rv)-positive control group, Arb low-dose(15 mg·kg~(-1)) group, and Arb high-dose(30 mg·kg~(-1)) group. The LPS-induced ALI model was established in all groups except the normal control group. Hematoxylin-eosin(HE) staining, TUNEL staining, and WBP whole-body non-invasive pulmonary function testing were used to evaluate the degree of lung tissue damage and lung function changes. Enzyme-linked immunosorbent assay(ELISA) was used to detect the level of inflammatory factors in lung tissue. Flow cytometry was used to analyze the M1/M2 polarization status of macrophages in lung tissue. Western blot was used to detect the expression levels of the TLR4 signaling pathway and related apoptotic proteins. Liquid chromatograph-mass spectrometer(LC-MS) metabolomics was used to analyze the changes in serum metabolic profile after Arb intervention. The results showed that Arb pretreatment significantly alleviated LPS-induced lung tissue injury, improved lung function, reduced the levels of pro-inflammatory factors(IL-6, TNF-α, IL-18, and IL-1β), and regulated the polarization status of M1/M2 macrophages. In addition, Arb inhibited the activation of the TLR4 signaling pathway, reduced the expression of pro-apoptotic proteins such as Bax, caspase-3, and caspase-9, up-regulated the level of Bcl-2 protein, and inhibited apoptosis of lung cells. Metabolomic analysis showed that Arb significantly improved LPS-induced metabolic abnormalities, mainly involving key pathways such as galactose metabolism, phenylalanine metabolism, and lipid metabolism. In summary, Arb can significantly reduce LPS-induced ALI by regulating the release of inflammatory factors, inhibiting the activation of the TLR4 signaling pathway, improving metabolic disorders, and regulating macrophage polarization, indicating that Arb has potential clinical application value.
Animals ; Acute Lung Injury/chemically induced* ; Mice ; Mice, Inbred BALB C ; Arbutin/administration & dosage* ; Male ; Toll-Like Receptor 4/immunology* ; Apoptosis/drug effects* ; Lung/metabolism* ; Signal Transduction/drug effects* ; Protective Agents/administration & dosage* ; Humans ; Macrophages/immunology* ; Drugs, Chinese Herbal/administration & dosage*

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

Extracellular vesicles (EVs) are crucial for facilitating intercellular communication, promoting cell migration, and orchestrating the immune response. Recently, EVs can diagnose and treat tumors. EVs can be measured as biomarkers to provide information about the type of disease and therapeutic efficacy. Furthermore, EVs with lower immunogenicity and better biocompatibility are natural carriers of chemicals and gene drugs. Herein, we review the molecular composition, biogenesis, and separation methods of EVs. We also highlight the important role of EVs from different origins as biomarkers and drug delivery systems in tumor therapy. Finally, we provide deep insights into how EVs play a role in reversing the immunosuppressive microenvironment.

Cardiac fibrosis is characterized by an elevated amount of extracellular matrix (ECM) within the heart. However, the persistence of cardiac fibrosis ultimately diminishes contractility and precipitates cardiac dysfunction. Circular RNAs (circRNAs) are emerging as important regulators of cardiac fibrosis. Here, we elucidate the functional role of a specific circular RNA CELF1 in cardiac fibrosis and delineate a novel feedback loop mechanism. Functionally, circ-CELF1 was involved in enhancing fibrosis-related markers' expression and promoting the proliferation of cardiac fibroblasts (CFs), thereby exacerbating cardiac fibrosis. Mechanistically, circ-CELF1 reduced the ubiquitination-degradation rate of BRPF3, leading to an elevation of BRPF3 protein levels. Additionally, BRPF3 acted as a modular scaffold for the recruitment of histone acetyltransferase KAT7 to facilitate the induction of H3K14 acetylation within the promoters of the Celf1 gene. Thus, the transcription of Celf1 was dramatically activated, thereby inhibiting the subsequent response of their downstream target gene Smad7 expression to promote cardiac fibrosis. Moreover, Celf1 further promoted Celf1 pre-mRNA transcription and back-splicing, thereby establishing a feedback loop for circ-CELF1 production. Consequently, a novel feedback loop involving CELF1/circ-CELF1/BRPF3/KAT7 was established, suggesting that circ-CELF1 may serve as a potential novel therapeutic target for cardiac fibrosis.

[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

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.