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.

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.

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.

Vaccine immunization is the most effective and cost-efficient method for infectious disease prevention and control.Since the outbreak of novel coronavirus pneumonia(caused by the novel coronavirus,COVID-19)at the end of 2019,third generation mRNA nucleic acid vaccines has been applied to stop viral spread.mRNA vaccines,rather than relying on the immune activation mode of traditional vaccines,are an innovative breakthrough using the body's own cells to produce antigens,thereby activating double specific immunity,forming immune memory,and providing more lasting specific immunity.Com-pared with the traditional first-generation(inactivated)and second-generation(genetically engineered)vaccines,mRNA vac-cines,because of the advantages provided by this platform,play important roles in the prevention and control of major sudden infectious diseases.Consequently,mRNA vaccines were the world's first COVID-19 vaccines to be applied clinically,thus ser-ving as a barometer in the field of vaccine research and development.Herein,the molecular design and presentation of mRNA vaccines,and the molecular mechanisms of their activation of the immune response are reviewed,to provide a theoretical basis for future application of novel mRNA vaccines in the prevention and control of animal infectious diseases.

Objective To analyze the research status,hot directions and frontier trends of traditional Chinese medicine in the prevention and treatment of diseases by regulating Notch signaling pathway based on bibliometrics.Methods Based on Citespace and Vosviewer,the literature on the regulation of Notch signaling pathway by traditional Chinese medicine in CNKI and WoSCC was visually analyzed.Results 362 and 85 related literatures were published in Chinese and English respectively until January 2024.Since 2013,the number of literatures published in this field has shown a fluctuating increasing trend.China is the country with the most publications;Hunan University of Chinese Medicine were the institutions with the most publications in the Chinese database,and Beijing University of Chinese Medicine was the institution with the most publications in the English literature database.Combined with the research direction of each research team and keyword clustering and burst analysis,the research hotspots of traditional Chinese medicine regulating Notch signaling pathway are focused on cerebral ischemia,myelosuppression and hepatic fibrosis.Diseases such as Asthma,colorectal cancer have become emerging research directions in recent years.Electroacupuncture therapy to promote stem cell proliferation and treat neurological diseases is one of the frontier research trends in this field.Conclusion Recent years have seen a rapid development of traditional Chinese medicine's disease prevention and treatment that targets Notch signaling pathway.Various expert teams have obtained rich research results,and the research hotspots show a diversified trend.In-depth exploration of this can provide strong evidence for the molecular mechanism of traditional Chinese medicine in the treatment of various diseases.

Acute respiratory distress syndrome(ARDS)is a common but critical respiratory disease that threatens human health.Its pathogenesis is complex,which brings great difficulties to the development of corresponding drugs and the exploration of treatment methods.Compared with two-dimensional cell culture and animal experiment models,lung organoid technology can realistically simulate the microen-vironment of human lung and shorten the research period,which has been rapidly developed in the research of lung diseases in recent years,and obtains many achievements in the related researches of ARDS.This article systematically reviews the application of lung organoids in the study of ARDS and analyzes its current limitations and main application directions in the future.