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.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveTo investigate the mechanism of Naoxintong capsules on ischemia-reperfusion （I/R） injury in rats based on the changes of pericytes mediated by Ras homolog family member A （RHOA）/Rho-associated coiled-coil containing protein kinase 1 （ROCK1） pathway. MethodsNinety rats （15 rats for each group） were randomly divided into a sham operation group， a model group， a positive control group receiving Ginkgo biloba extract （21.6 mg·kg^-1）， and groups receiving Naoxintong capsules at low， medium， and high doses of 55， 110， and 220 mg·kg^-1 （NXT-L， NXT-M， and NXT-H groups）， respectively. Except for those in the sham operation group， all rats were subjected to transient middle cerebral artery occlusion （tMCAO） to establish the experiment model. Nerve function was assessed using a neurological function score. Cerebral blood flow was detected using a laser speckle contrast imager， and the cerebral infarction rate was calculated using 2，3，5-Triphenyl tetrazolium chloride （TTC） staining. Pathological changes were observed by hematoxylin-eosin （HE） staining and Nissl staining， while pericyte morphology was observed via transmission electron microscopy. Blood-brain barrier destruction was observed by Evans blue staining. Albumin and ischemia-modified albumin levels were measured using assay kits. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to detect the mRNA and protein expression levels of RHOA， ROCK1， platelet-derived growth factor receptor β （PDGFRB）， α-smooth muscle actin （α-SMA）， tight junction protein （ZO-1）， matrix metalloproteinase-2 （MMP-2）， and matrix metalloproteinase-9 （MMP-9）. ResultsCompared with the sham operation group， the model group exhibited decreased neurological function scores， higher percentage reduction in blood flow， and increased cerebral infarction rates （P<0.01）. Additionally， cortical neuronal nucleus shrinkage， edema， a decreased number of Nissl bodies， reduced pericyte area， elevated albumin content in the cortex （P<0.05）， and increased ischemic modified albumin levels （P<0.01） were observed. The mRNA and protein expression levels of RHOA， ROCK1， PDGFRB， α-SMA， MMP-2， and MMP-9 were increased （P<0.01）， while those of ZO-1 were decreased. Compared with the model group， all treatment groups showed improved neurological function scores， lower percentage reduction in blood flow， reduced cerebral infarction rates （P<0.01）， alleviated cortical histological changes， increased number of Nissl bodies， expanded pericyte area， decreased albumin content in the cortex， and reduced ischemia-modified albumin levels （P<0.01）. The mRNA and protein expression levels of RHOA， ROCK1， PDGFRB， α-SMA， MMP-2， and MMP-9 were decreased （P<0.01）， while those of ZO-1 were increased. Among the treatment groups， the NXT-M group showed the most pronounced improvement in cerebral I/R injury. ConclusionNaoxintong capsules can restore cerebral blood supply， reduce microcirculation disturbance， and protect blood-brain barrier in rats with I/R injury. Its mechanism of action may be related to the inhibition of the RHOA/ROCK1 signaling pathway and reduced pericyte contraction.

Spinal cord injury(SCI)is a central nervous system disease with high morbidity and disability rates,bringing serious economic and psychological burdens to families and society worldwide.AMP-activated protein kinase(AMPK)is an important sensor in the energy metabolism process in living organisms,which plays a central role in maintaining energy balance.It is currently considered a key target for the prevention and treatment of multiple diseases.Studies have shown that AMPK signaling can regulate autophagy,neuroinflammation,oxidative stress,mitochondrial function and other processes after SCI,thus affecting the pathological process of SCI.This review summarizes the research progress on AMPK signaling pathway involved in the regulation of SCI,in order to provide new ideas for the treatment and drug development of SCI.

Alzheimer's disease (AD), a prototypical neurodegenerative disorder, encompasses multifaceted pathological processes. As pivotal cellular structures within the central nervous system, ion channels play critical roles in regulating neuronal excitability, synaptic transmission, and neurotransmitter release. Extensive research has revealed significant alterations in the expression and function of ion channels in AD, implicating an important role of ion channels in the pathogenesis of abnormal Aβ deposition, neuroinflammation, oxidative stress, and disruptions in calcium homeostasis and neural network functionality. This review systematically summarizes the crucial roles and underlying mechanisms of ion channels in the onset and progression of AD, highlighting how these channel abnormalities contribute to AD pathophysiology. We also discuss the therapeutic potential of ion channel modulation in AD treatment, emphasizing the importance of addressing multifactorial nature and heterogeneity of AD. The development of multi-target drugs and precision therapies is proposed as a future direction of scientific research.
Alzheimer Disease/therapy* ; Humans ; Ion Channels/physiology* ; Oxidative Stress ; Animals ; Amyloid beta-Peptides/metabolism* ; Synaptic Transmission ; Calcium/metabolism*

This study aims to explore whether Naoxintong Capsules improve multiple cerebral infarctions and myocardial injury via promoting angiogenesis, thereby exerting a simultaneous treatment effect on both the brain and heart. Male SD rats were randomly divided into six groups: sham-operated group, model group, high-dose, medium-dose, and low-dose groups of Naoxintong Capsules(440, 220, and 110 mg·kg~(-1)), and nimodipine group(10.8 mg·kg~(-1)). Rat models of multiple cerebral infarctions were established by injecting autologous thrombus, and samples were collected and tested seven days after modeling. Evaluations included multiple cerebral infarction model assessments, neurological function scores, grip strength tests, and rotarod tests, so as to evaluate neuromotor functions. Morphological structures of brain and heart tissue were observed using hematoxylin-eosin(HE) staining, Nissl staining, and Masson staining. Network pharmacology was employed to screen the mechanisms of Naoxintong Capsules in improving multiple cerebral infarctions and myocardial injury. Neuronal and myocardial cell ultrastructures were observed using transmission electron microscopy. Apoptosis rate in brain neuronal cells was detected by TdT-mediated dUTP nick end labeling(TUNEL) staining, and reactive oxygen species(ROS) levels in myocardial cells were measured. Immunofluorescence was used to detect the expression of platelet endothelial cell adhesion molecule-1(CD31), antigen identified by monoclonal antibody Ki67(Ki67), hematopoietic progenitor cell antigen CD34(CD34), and hypoxia inducible factor-1α(HIF-1α) in brain and myocardial tissue. Western blot, and real-time quantitative polymerase chain reaction(RT-qPCR) were used to detect the expression of HIF-1α, vascular endothelial growth factor(VEGF), vascular endothelial growth factor receptor 2(VEGFR2), sarcoma(Src), basic fibroblast growth factor(bFGF), angiopoietin-1(Ang-1), and TEK receptor tyrosine kinase(Tie-2). Compared with the model group, the medium-dose group of Naoxintong Capsules showed significantly lower neurological function scores, increased grip strength, and prolonged time on the rotarod. Pathological damage in brain and heart tissue was reduced, with increased and more orderly arranged mitochondria in neurons and cardiomyocytes. Apoptosis in brain neuronal cells was decreased, and ROS levels in cardiomyocytes were reduced. The microvascular density and endothelial cells of new blood vessels in brain and heart tissue increased, with increased overlapping regions of CD31 and Ki67 expression. The relative protein and mRNA expression levels of HIF-1α, VEGF, VEGFR2, Src, Ang-1, Tie-2, and bFGF were elevated in brain tissue and myocardial tissue. Naoxintong Capsules may improve multiple cerebral infarctions and myocardial injury by mediating HIF-1α/VEGF expression to promote angiogenesis.
Animals ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Cerebral Infarction/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Vascular Endothelial Growth Factor A/genetics* ; Capsules ; Signal Transduction/drug effects* ; Humans ; Brain/metabolism* ; Myocardium/metabolism* ; Apoptosis/drug effects*

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.

ObjectiveTo investigate the mechanism of Naoxintong capsules on ischemia-reperfusion （I/R） injury in rats based on the changes of pericytes mediated by Ras homolog family member A （RHOA）/Rho-associated coiled-coil containing protein kinase 1 （ROCK1） pathway. MethodsNinety rats （15 rats for each group） were randomly divided into a sham operation group， a model group， a positive control group receiving Ginkgo biloba extract （21.6 mg·kg^-1）， and groups receiving Naoxintong capsules at low， medium， and high doses of 55， 110， and 220 mg·kg^-1 （NXT-L， NXT-M， and NXT-H groups）， respectively. Except for those in the sham operation group， all rats were subjected to transient middle cerebral artery occlusion （tMCAO） to establish the experiment model. Nerve function was assessed using a neurological function score. Cerebral blood flow was detected using a laser speckle contrast imager， and the cerebral infarction rate was calculated using 2，3，5-Triphenyl tetrazolium chloride （TTC） staining. Pathological changes were observed by hematoxylin-eosin （HE） staining and Nissl staining， while pericyte morphology was observed via transmission electron microscopy. Blood-brain barrier destruction was observed by Evans blue staining. Albumin and ischemia-modified albumin levels were measured using assay kits. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to detect the mRNA and protein expression levels of RHOA， ROCK1， platelet-derived growth factor receptor β （PDGFRB）， α-smooth muscle actin （α-SMA）， tight junction protein （ZO-1）， matrix metalloproteinase-2 （MMP-2）， and matrix metalloproteinase-9 （MMP-9）. ResultsCompared with the sham operation group， the model group exhibited decreased neurological function scores， higher percentage reduction in blood flow， and increased cerebral infarction rates （P<0.01）. Additionally， cortical neuronal nucleus shrinkage， edema， a decreased number of Nissl bodies， reduced pericyte area， elevated albumin content in the cortex （P<0.05）， and increased ischemic modified albumin levels （P<0.01） were observed. The mRNA and protein expression levels of RHOA， ROCK1， PDGFRB， α-SMA， MMP-2， and MMP-9 were increased （P<0.01）， while those of ZO-1 were decreased. Compared with the model group， all treatment groups showed improved neurological function scores， lower percentage reduction in blood flow， reduced cerebral infarction rates （P<0.01）， alleviated cortical histological changes， increased number of Nissl bodies， expanded pericyte area， decreased albumin content in the cortex， and reduced ischemia-modified albumin levels （P<0.01）. The mRNA and protein expression levels of RHOA， ROCK1， PDGFRB， α-SMA， MMP-2， and MMP-9 were decreased （P<0.01）， while those of ZO-1 were increased. Among the treatment groups， the NXT-M group showed the most pronounced improvement in cerebral I/R injury. ConclusionNaoxintong capsules can restore cerebral blood supply， reduce microcirculation disturbance， and protect blood-brain barrier in rats with I/R injury. Its mechanism of action may be related to the inhibition of the RHOA/ROCK1 signaling pathway and reduced pericyte contraction.