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 construct a refined management model based on the Interrupt Time Series(ITS)algorithm model for medical consumables,so as to provide a scientific basis for hospitals in implementing refined management for medical consumables.Methods:The supply,inventory and usage data of medical consumables were collected,and the ITS algorithm was used to analyze the prediction for the usage of medical consumables,and the influence of policy intervention.The outliers and key influencing factors of the full-cycle data of medical consumables were mined,and management countermeasures were formulated from three aspects:early warning for dynamic inventory,feedback of policy effect,and abnormal rectification handling.A total of 10,614 medical consumables that were routinely used at Taizhou People's Hospital from January 2023 to December 2024 were selected.The empirical management method was adopted to manage 4,924 medical consumables during the period from January to December 2023,and the refined management method was adopted to manage 5,690 medical consumables during the period from January to December 2024.The utilization rate of medical consumables,the quality of management decisions and the performance of clinical services of the two management methods were compared.Results:The average inventory turnover rate and usage rate of medical consumables of the refined management method were respectively(91.11±3.02)%and(91.19±2.75)%,which were higher than those of the empirical management method.The proportion of average inventory cost,and loss rate of overstock of the refined management method were respectively(2.53±0.57)%and(2.84±0.44)%,which were lower than those of the empirical management method,and the differences were all statistically significant(t=4.698,5.976,4.518,8.496,P<0.05).The average accuracy rate of prediction,detection rate of abnormity,and the mean of timely handling rate of the refined management method for medical consumables were all higher than those of the empirical management method,and the differences were statistically significant(t=6.423,8.587,6.102,P<0.05).The satisfaction of doctors,nurses,warehouse managers and purchasing managers who used and managed medical consumables for the clinical service performance of adopting the refined management method was higher than that of adopting the empirical management method,and the differences were statistically significant(x2=7.416,12.793,4.267,4.667,P<0.05).Conclusion:The application of the refined management model based on the ITS algorithm model for medical consumables can enhance the predictive ability for the demand of medical staffs for medical consumables,and reduce the pressure for inventory,and management costs of medical consumables,and accurately detect abnormal problems in the use of medical consumables,and improve the level of clinical services.

Intestinal fibrosis is a significant clinical challenge in inflammatory bowel diseases, but no effective anti-fibrotic therapy is currently available. Glucagon receptor (GCGR) and glucagon-like peptide 1 receptor (GLP1R) are both peptide hormone receptors involved in energy metabolism of epithelial cells. However, their role in intestinal fibrosis and the underlying mechanisms remain largely unexplored. Herein GCGR and GLP1R were found to be reduced in the stenotic ileum of patients with Crohn's disease as well as in the fibrotic colon of mice with chronic colitis. The downregulation of GCGR and GLP1R led to the accumulation of the metabolic byproduct lactate, resulting in histone H3K9 lactylation and exacerbated intestinal fibrosis through epithelial-to-mesenchymal transition (EMT). Dual activating GCGR and GLP1R by peptide 1907B reduced the H3K9 lactylation in epithelial cells and ameliorated intestinal fibrosis in vivo. We uncovered the role of GCGR/GLP1R in regulating EMT involved in intestinal fibrosis via histone lactylation. Simultaneously activating GCGR/GLP1R with the novel dual agonist peptide 1907B holds promise as a treatment strategy for alleviating intestinal fibrosis.

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.

Utilizing network pharmacology, molecular docking, and cellular experimental validation, this study delved into the therapeutic efficacy and underlying mechanisms of matrine in combating senescence. Databases were utilized to predict targets related to the anti-senescence effects of matrine, resulting in the identification of 81 intersecting targets for matrine in the treatment of senescence. A protein-protein interaction(PPI) network was constructed, and key targets were screened based on degree values. Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses were performed on the key targets to elucidate the critical pathways involved in the anti-senescence effects of matrine. Molecular docking was conducted between matrine and key targets. A senescence model was established using human umbilical vein endothelial cells(HUVECs) induced with hydrogen peroxide(H_2O_2). Following treatment with varying concentrations of matrine(0.5, 1, and 2 mmol·L~(-1)), cell viability was assessed by using the CCK-8. SA-β-galactosidase staining was employed to observe the positive rate of senescent cells. Flow cytometry was utilized to measure the apoptosis rate. Real-time quantitative PCR(RT-PCR) was utilized to measure the mRNA expression of apoptosis-related cysteine peptidase 3(CASP3), albumin(ALB), glycogen synthase kinase 3β(GSK3B), CD44 molecule(CD44), and tumor necrosis factor-α(TNF-α). Western blot was performed to detect the protein expression of tumor protein p53(p53), cyclin-dependent kinase inhibitor 1A(p21), cyclin-dependent kinase inhibitor 2A(p16), and retinoblastoma tumor suppressor protein(pRb) in the senescence signaling pathway, p38 protein kinase(p38), c-Jun N-terminal kinase(JNK), and extracellular regulated protein kinases(ERK) in the mitogen-activated protein kinase(MAPK) pathway, and phosphatidylinositol 3-kinase(PI3K) and protein kinase B(Akt) in the PI3K/Akt signaling pathway. The experimental results revealed that matrine significantly increased the viability of HUVECs(P<0.05), decreased the positive rate of senescent cells and the apoptosis rate(P<0.05), and reduced the mRNA expression levels of CASP3, ALB, GSK3B, CD44, and TNF-α(P<0.05). It also inhibited the protein expression of p53, p21, p16 and pRb in the senescence signaling pathway(P<0.05), upregulated the protein expression of p-PI3K/PI3K and p-Akt/Akt(P<0.05), and downregulated the protein expression of p-p38/p38, p-JNK/JNK, and p-ERK/ERK(P<0.05). Collectively, these findings suggest that matrine exerts an inhibitory effect on HUVECs senescence, and its mechanism involves the modulation of the senescence signaling pathway, MAPK pathway, and PI3K/Akt signaling pathway to suppress cell apoptosis and inflammation.
Humans ; Matrines ; Quinolizines/chemistry* ; Alkaloids/chemistry* ; Human Umbilical Vein Endothelial Cells/cytology* ; Cellular Senescence/drug effects* ; Network Pharmacology ; Molecular Docking Simulation ; Signal Transduction/drug effects* ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Apoptosis/drug effects* ; Drugs, Chinese Herbal/pharmacology*

The interactions between microorganisms and medicinal plants are crucial to the quality improvement of medicinal plants. Medicinal plants attract microorganisms to colonize by secreting specific compounds and provide niche and nutrient support for these microorganisms, with a symbiotic network formed. These microorganisms grow in the rhizosphere, phyllosphere, and endophytic tissues of plants and significantly improve the growth performance and medicinal component accumulation of medicinal plants by promoting nutrient uptake, enhancing disease resistance, and regulating the synthesis of secondary metabolites. Microorganisms are also widely used in the ecological planting of medicinal plants, and the growth conditions of medicinal plants are optimized by simulating the microbial effects in the natural environment. The interactions between microorganisms and medicinal plants not only significantly improve the yield and quality of medicinal plants but also enhance their geoherbalism, which is in line with the concept of green agriculture and eco-friendly development. This study reviewed the research results on the interactions between medicinal plants and microorganisms in recent years and focused on the analysis of the great potential of microorganisms in optimizing the growth environment of medicinal plants, regulating the accumulation of secondary metabolites, inducing systemic resistance, and promoting the ecological planting of medicinal plants. It provides a scientific basis for the research on the interactions between medicinal plants and microorganisms, the research and development of microbial agents, and the application of microorganisms in the ecological planting of medicinal plants and is of great significance for the quality improvement of medicinal plants and the green and sustainable development of TCM resources.
Plants, Medicinal/metabolism* ; Bacteria/genetics* ; Symbiosis

Stem cell treatment may enhance erectile dysfunction (ED) in individuals with cavernous nerve injury (CNI). Nevertheless, no investigations have directly ascertained the implications of varying amounts of human umbilical cord-derived mesenchymal stem cells (HUC-MSCs) on ED. We compare the efficacy of three various doses of HUC-MSCs as a therapeutic strategy for ED. Sprague-Dawley rats (total = 175) were randomly allocated into five groups. A total of 35 rats underwent sham surgery and 140 rats endured bilateral CNI and were treated with vehicles or doses of HUC-MSCs (1 × 10 6 cells, 5 × 10 6 cells, and 1 × 10 7 cells in 0.1 ml, respectively). Penile tissues were harvested for histological analysis on 1 day, 3 days, 7 days, 14 days, 28 days, 60 days, and 90 days postsurgery. It was found that varying dosages of HUC-MSCs enhanced the erectile function of rats with bilateral CNI and ED. Moreover, there was no significant disparity in the effectiveness of various dosages of HUC-MSCs. However, the expression of endothelial markers (rat endothelial cell antigen-1 [RECA-1] and endothelial nitric oxide synthase [eNOS]), smooth muscle markers (alpha smooth muscle actin [α-SMA] and desmin), and neural markers (neurofilament [RECA-1] and neurogenic nitric oxide synthase [nNOS]) increased significantly with prolonged treatment time. Masson's staining demonstrated an increased in the smooth muscle cell (SMC)/collagen ratio. Significant changes were detected in the microstructures of various types of cells. In vivo imaging system (IVIS) analysis showed that at the 1 st day, the HUC-MSCs implanted moved to the site of damage. Additionally, the oxidative stress levels were dramatically reduced in the penises of rats administered with HUC-MSCs.
Male ; Animals ; Erectile Dysfunction/metabolism* ; Rats, Sprague-Dawley ; Mesenchymal Stem Cell Transplantation/methods* ; Rats ; Penis/pathology* ; Humans ; Disease Models, Animal ; Umbilical Cord/cytology* ; Peripheral Nerve Injuries/complications* ; Mesenchymal Stem Cells ; Nitric Oxide Synthase Type III/metabolism* ; Actins/metabolism* ; Nitric Oxide Synthase Type I/metabolism*

BACKGROUND: Cardiovascular disease (CVD) is the predominant cause of mortality in China. However, the mechanisms linking sarcopenia to CVD remain poorly understood, particularly in normal-weight populations. Individuals with the absence of overweight or obesity may tend to experience missed opportunities for timely intervention. This study aimed to investigate the longitudinal association between sarcopenia and incidence of new-onset CVD in a normal-weight population, and to examine the mediating effect of functional limitation in this relationship. METHODS: We conducted a closed-cohort analysis using a nationwide sample of 4,147 middle-aged and older adults with normal weight in China. We performed Cox proportional hazards regression analysis to explore the associations of baseline sarcopenia with incident CVD. The difference method was applied to estimate the mediation proportion of functional limitation in this association. RESULTS: Over a mean follow-up period of 7.62 years, CVD occurred in 835 participants. In the multivariable-adjusted Cox model, individuals with sarcopenia exhibited a significantly higher likelihood of developing incident CVD compared to those without sarcopenia (adjusted hazard ratio [aHR] = 1.45, 95% confidence interval [CI]: 1.21-1.73, P < 0.001). Similar associations were observed for the incidence of heart disease and stroke. Functional limitation accounted for approximately 15.0% of the total effect of sarcopenia on incident CVD (P < 0.001). CONCLUSIONS Sarcopenia exerts both direct and indirect effects on incident CVD among middle-aged and older adults who are normal weight, with functional limitation serving as a significant mediator. Interventions targeting both sarcopenia and functional limitation may offer a promising strategy for enhancing cardiovascular health in this population.
Humans ; Sarcopenia/complications* ; China/epidemiology* ; Male ; Female ; Middle Aged ; Cardiovascular Diseases/etiology* ; Aged ; Incidence ; Cohort Studies ; Proportional Hazards Models ; Risk Factors ; Aged, 80 and over ; Longitudinal Studies

Objective:To construct a refined management model based on the Interrupt Time Series(ITS)algorithm model for medical consumables,so as to provide a scientific basis for hospitals in implementing refined management for medical consumables.Methods:The supply,inventory and usage data of medical consumables were collected,and the ITS algorithm was used to analyze the prediction for the usage of medical consumables,and the influence of policy intervention.The outliers and key influencing factors of the full-cycle data of medical consumables were mined,and management countermeasures were formulated from three aspects:early warning for dynamic inventory,feedback of policy effect,and abnormal rectification handling.A total of 10,614 medical consumables that were routinely used at Taizhou People's Hospital from January 2023 to December 2024 were selected.The empirical management method was adopted to manage 4,924 medical consumables during the period from January to December 2023,and the refined management method was adopted to manage 5,690 medical consumables during the period from January to December 2024.The utilization rate of medical consumables,the quality of management decisions and the performance of clinical services of the two management methods were compared.Results:The average inventory turnover rate and usage rate of medical consumables of the refined management method were respectively(91.11±3.02)%and(91.19±2.75)%,which were higher than those of the empirical management method.The proportion of average inventory cost,and loss rate of overstock of the refined management method were respectively(2.53±0.57)%and(2.84±0.44)%,which were lower than those of the empirical management method,and the differences were all statistically significant(t=4.698,5.976,4.518,8.496,P<0.05).The average accuracy rate of prediction,detection rate of abnormity,and the mean of timely handling rate of the refined management method for medical consumables were all higher than those of the empirical management method,and the differences were statistically significant(t=6.423,8.587,6.102,P<0.05).The satisfaction of doctors,nurses,warehouse managers and purchasing managers who used and managed medical consumables for the clinical service performance of adopting the refined management method was higher than that of adopting the empirical management method,and the differences were statistically significant(x2=7.416,12.793,4.267,4.667,P<0.05).Conclusion:The application of the refined management model based on the ITS algorithm model for medical consumables can enhance the predictive ability for the demand of medical staffs for medical consumables,and reduce the pressure for inventory,and management costs of medical consumables,and accurately detect abnormal problems in the use of medical consumables,and improve the level of clinical services.