Myocardial infarction （MI） and osteoporosis （OP）， as two prevalent metabolic diseases with high morbidity and mortality rates， are respectively characterized by cardiovascular system dysfunction and bone homeostasis imbalance， collectively posing significant global public health challenges. While clinically often considered as independent diseases， recent studies have revealed shared pathological mechanisms between the two. This study initiated its exploration from the traditional Chinese medicine concept of the "heart-bone" axis， systematically analyzing the correlation between MI and OP from perspectives including hemodynamics， neuroendocrinology， calcium homeostasis， inflammation and vascular injury， as well as hormone levels. By discussing the pathological mechanisms of "heart disease affecting the bones and bone disease affecting the heart"， the study also elucidated advancements in both Western and traditional Chinese medicine treatments. The goal is to provide novel insights and methodologies for the prevention and treatment of "heart-bone comorbidities"， thereby facilitating comprehensive management of cardiovascular and skeletal diseases.

Myocardial infarction （MI） and osteoporosis （OP）， as two prevalent metabolic diseases with high morbidity and mortality rates， are respectively characterized by cardiovascular system dysfunction and bone homeostasis imbalance， collectively posing significant global public health challenges. While clinically often considered as independent diseases， recent studies have revealed shared pathological mechanisms between the two. This study initiated its exploration from the traditional Chinese medicine concept of the "heart-bone" axis， systematically analyzing the correlation between MI and OP from perspectives including hemodynamics， neuroendocrinology， calcium homeostasis， inflammation and vascular injury， as well as hormone levels. By discussing the pathological mechanisms of "heart disease affecting the bones and bone disease affecting the heart"， the study also elucidated advancements in both Western and traditional Chinese medicine treatments. The goal is to provide novel insights and methodologies for the prevention and treatment of "heart-bone comorbidities"， thereby facilitating comprehensive management of cardiovascular and skeletal diseases.

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 effect and mechanism of Tangzhi formula（TZF） on improving glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats.METHODS Thirty SPF-grade SD rats were used to establish a T2DM rat model through high-fat diet feeding combined with intraperitoneal injection of streptozotocin. After successful modeling， the rats were divided into the model group， metformin group （positive control， 0.18 g/kg）， and TZF low-， medium-， and high-dose groups （5， 10， and 20 g/kg）， with 6 rats in each group. An additional 6 rats were selected as the blank group. The rats in each group were orally administered the corresponding volume of drug solution or saline， and continuous dosing was performed for 6 weeks， once daily. Fasting blood glucose （FBG）， area under the curve of oral glucose tolerance test （OGTT-AUC）， area under the curve of intraperitoneal insulin tolerance test （IPITT-AUC）， serum total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） levels were measured. Hematoxylin-eosin （HE） staining and oil red O staining were used to observe hepatic pathological morphology and lipid deposition. Differentially expressed proteins in liver tissues were screened by proteomics analysis， and key proteins in signaling pathways were verified by Western blot and immunofluorescence staining.RESULTS Compared with the blank group， the levels of FBG， OGTT-AUC， IPITT-AUC， LDL-C， TG， and TC in the model group were significantly increased （ P ＜0.05）， while HDL-C level was significantly decreased （ P ＜0.05）； the liver tissues showed disordered hepatic cords， punctate necrosis of hepatocytes， and significant lipid deposition， the NAFLD activity score and positive area of lipid droplets were significantly increased（ P ＜0.05）. Compared with the model group， the above indicators in TZF low-， medium-， and high-dose groups and metformin group were significantly reversed （ P ＜0.05）； hepatic pathological injury and lipid deposition were alleviated. Proteomics results showed that a total of 442 differentially expressed proteins were screened between the model group and the blank group， and 358 differentially expressed proteins were screened between the TZF high-dose group and the model group. Among these differentially expressed proteins， multiple proteins closely related to glucose and lipid metabolism showed a reversal trend after TZF intervention. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the peroxisome proliferator-activated receptor （PPAR） signaling pathway was significantly enriched. The validation experiment results showed that TZF could significantly up-regulate the expression of PPARα， PPARγ， and apolipoprotein A1 （ApoA1） in rat liver tissues （ P ＜0.05）， and down-regulate the expression of stearoyl-CoA desaturase 1 （SCD1） and perilipin 2 （PLIN2） （ P ＜0.05）.CONCLUSIONS TZF can improve glucose and lipid metabolism disorders in T2DM rats； its mechanism may be related to the activation of the PPAR signaling pathway and the regulation of downstream lipid metabolism-related proteins SCD1， PLIN2， and ApoA1.

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.

Medical imaging technology plays a crucial role in clinical diagnosis and treatment. Image compression technology provides robust technical support for the storage and transmission of massive medical imaging data, serving as an effective safeguard for hospital data backup and telemedicine. The technology holds broad application prospects in the medical field, enabling the processing of various imaging modalities, multidimensional imaging, and medical video imaging. This study elaborates on general image and video compression algorithms, the application of compression algorithms in the medical field, and the performance metrics of medical image compression, thereby providing critical technical support for enhancing clinical diagnostic efficiency and data management security.

The present study aimed to investigate the effects of eccentric treadmill exercise on adaptive hypertrophy of skeletal muscle in rats. Thirty-two 3-month-old Sprague Dawley (SD) rats were selected and randomly assigned to one of the four groups based on their body weights: 2-week quiet control group (2C), 2-week downhill running exercise group (2E), 4-week quiet control group (4C), and 4-week downhill running exercise group (4E). The downhill running protocol for rats in the exercise groups involved slope of -16°, running speed of 16 m/min, training duration of 90 min, and 5 training sessions per week. Twenty-four hours after the final session of training, all the four groups of rats underwent an exhaustion treadmill exercise. After resting for 48 h, all the rats were euthanized and their gastrocnemius muscles were harvested for analysis. HE staining was used to measure the cross-sectional area (CSA) and diameter of muscle fibers. Transmission electron microscope was used to observe the ultrastructural changes in muscle fibers. Purithromycin surface labeling translation method was used to measure protein synthesis rate. Immunofluorescence double labeling was used to detect the colocalization levels of lysosomal-associated membrane protein 2 (Lamp2)-leucyl-tRNA synthetase (LARS) and Lamp2-mammalian target of rapamycin (mTOR). Western blot was used to measure the protein expression levels of myosin heavy chain (MHC) IIb and LARS, as well as the phosphorylation levels of mTOR, p70 ribosomal protein S6 kinase (p70S6K), and eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1). The results showed that, compared with the 2C group rats, the 2E group rats showed significant increases in wet weight of gastrocnemius muscle, wet weight/body weight ratio, running distance, running time, pre- and post-exercise blood lactate levels, myofibrillar protein content, colocalization levels of Lamp2-LARS and Lamp2-mTOR, and LARS protein expression. Besides these above changes, compared with the 4C group, the 4E group further exhibited significantly increased fiber CSA, fiber diameter, protein synthesis rate, and phosphorylation levels of mTOR, p70S6K, and 4E-BP1. Compared with the quiet control groups, the exercise groups exhibited ultrastructural damage of rat gastrocnemius muscle, which was more pronounced in the 4E group. These findings suggest that eccentric treadmill exercise may promote mTOR translocation to lysosomal membrane, activating mTOR signaling via up-regulating LARS expression. This, in turn, increases protein synthesis rate through the mTOR-p70S6K-4E-BP1 signaling pathway, promoting protein deposition and inducing adaptive skeletal muscle hypertrophy. Although the ultrastructural changes of skeletal muscle are more pronounced, the relatively long training cycles during short-term exercise periods have a more significant effect on promoting gastrocnemius muscle protein synthesis and adaptive hypertrophy.
Animals ; Rats, Sprague-Dawley ; Physical Conditioning, Animal/physiology* ; Rats ; Muscle, Skeletal/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Male ; Hypertrophy ; Adaptation, Physiological/physiology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Ribosomal Protein S6 Kinases, 70-kDa/metabolism* ; Intracellular Signaling Peptides and Proteins

This paper aims to study the effect of Ziziphi Spinosae Semen extracts on chronic unpredictable mild stress(CUMS)-induced depression-like and insomnia behavior models of mice. The CUMS-induced depression-like and insomnia behavior model of mice was established by CUMS treatment for three weeks. The mice were randomly divided into control group, model group, positive drug diazepam group(2 mg·kg~(-1)), as well as low-dose group(1.95 g·kg~(-1)), medium-dose group(3.9 g·kg~(-1)), and high-dose group(7.8 g·kg~(-1)) of Ziziphi Spinosae Semen extracts, with 18 mice in each group. On the 15th day of modeling, the drug was administered intragastrically once a day for one week. Then, the pentobarbital sodium cooperative righting experiment, open field experiment, and elevated plus maze experiment were carried out, respectively. The contents of neurotransmitters 5-hydroxytryptamine(5-HT) and 5-hydroxyindoleacetic acid(5-HIAA) in serum and thalamus of mice, as well as the levels of corticotropin releasing hormone(CRH), adrenocorticotropic hormone(ACTH), and corticosterone(CORT) in serum, were determined by enzyme-linked immunosorbent assay(ELISA). The neuron damage in the hippocampus of mice was observed by hematoxylin-eosin(HE) staining and Nissl staining. Western blot was used to detect the expressions of tryptophan hydroxylase 2(TPH2), serotonin transporter(SERT), monoamine oxidase A(MAOA), five prime repressors under dual repression binding protein 1(Freud1), synaptic plasticity-related proteins [cellular gene FOS(C-FOS), postsynaptic density protein 95(PSD95), synapsin 1(SYN1), and activity-regulated cytoskeleton-associated gene(ARC)], blood-brain barrier(BBB) permeability-related proteins [zonula occludens 1(ZO-1), occludin, and claudin 1], inflammatory factors [NOD-, LRR-and pyrin domain-containing protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), gasdermin D(GSDMD), caspase-3, and caspase-8], and antioxidant factors [nuclear factor erythroid 2-related factor 2(NRF2) and heme oxygenase 1(HO1)] in thalamic tissue of mice. The results indicated that compared with that in the model group, the sleep latency was significantly shortened, and the sleep duration was significantly prolonged in each dose group of Ziziphi Spinosae Semen extracts. The number of visits to the central area of the open field and the distance and time of visits were significantly increased in each dose group of Ziziphi Spinosae Semen extracts. In addition, the proportion of distance and time of entering the open arm area of the elevated plus maze was significantly increased in each dose group of Ziziphi Spinosae Semen extracts. The contents of 5-HT and 5-HIAA in serum and thalamus of mice increased to varying degrees in each dose group of Ziziphi Spinosae Semen extracts; the contents of CRH, ACTH, and CORT in serum of mice were significantly decreased. The protein expression of TPH2 was significantly increased. The protein expression of MAOA, SERT, and Freud1 was significantly decreased. Ziziphi Spinosae Semen extracts could also significantly reduce the protein expression of C-FOS but significantly increase the protein expression of PSD95, ARC, and SYN1. They could reduce the pathological damage of the hippocampus in mice and significantly increase the protein expression of ZO-1, occluding, and claudin 1. The protein expression of NLRP3, GSDMD, ASC, caspase-3, and caspase-8 in the thalamic tissue of mice was significantly decreased, and the protein expression of HO1 and NRF2 was significantly increased. In conclusion, Ziziphi Spinosae Semen extracts could effectively improve sleep disorders and depression-like behaviors in CUMS-induced model mice, which may be related to regulating the 5-HT anabolism process and hypothalamic-pituitary-adrenal(HPA) axis-related hormone levels, reducing pathological damage in the hippocampus, improving synaptic plasticity, repairing BBB integrity, and alleviating inflammatory response and oxidative stress damage.
Animals ; Ziziphus/chemistry* ; Mice ; Male ; Depression/psychology* ; Drugs, Chinese Herbal/administration & dosage* ; Sleep Initiation and Maintenance Disorders/psychology* ; Stress, Psychological/complications* ; Behavior, Animal/drug effects* ; Humans ; Disease Models, Animal

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.