ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

This study aims to explore the mechanism of Buyang Huanwu Decoction(BHD) in promoting angiogenesis after oxygen-glucose deprivation/reoxygenation(OGD/R) of mouse brain microvascular endothelial cell line(brain-derived Endothelial cells.3, bEnd.3) based on the caveolin-1(Cav1)/Yes-associated protein 1(YAP1)/hypoxia-inducible factor-1α(HIF-1α) signaling pathway. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was used to analyze the blood components of BHD. The cell counting kit-8(CCK-8) method was used to detect the optimal intervention concentration of drug-containing serum of BHD after OGD/R injury of bEnd.3. The lentiviral transfection method was used to construct a Cav1 silent stable strain, and Western blot and polymerase chain reaction(PCR) methods were used to verify the silencing efficiency. The control bEnd.3 cells were divided into a normal group(sh-NC control group), an OGD/R model + blank serum group(sh-NC OGD/R group), and an OGD/R model + drug-containing serum group(sh-NC BHD group). Cav1 silent cells were divided into an OGD/R model + blank serum group(sh-Cav1 OGD/R group) and an OGD/R model + drug-containing serum group(sh-Cav1 BHD group). The cell survival rate was detected by the CCK-8 method. The cell migration ability was detected by a cell migration assay. The lumen formation ability was detected by an angiogenesis assay. The apoptosis rate was detected by flow cytometry, and the expression of YAP1/HIF-1α signaling pathway-related proteins in each group was detected by Western blot. Finally, co-immunoprecipitation was used to verify the interaction between YAP1 and HIF-1α. The results showed astragaloside Ⅳ, formononetin, ferulic acid, and albiflorin in BHD can all enter the blood. The drug-containing serum of BHD at a mass fraction of 10% may be the optimal intervention concentration for OGD/R-induced injury of bEnd.3 cells. Compared with the sh-NC control group, the sh-NC OGD/R group showed significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, significantly increased cell apoptotic rate, significantly lowered phosphorylation level of YAP1 at S127 site, and significantly elevated nuclear displacement level of YAP1 and protein expression of HIF-1α, vascular endothelial growth factor(VEGF), and vascular endothelial growth factor receptor 2(VEGFR2). Compared with the same type of OGD/R group, the sh-NC BHD group and sh-Cav1 BHD group had significantly increased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly decreased cell apoptotic rate, a further decreased phosphorylation level of YAP1 at S127 site, and significantly increased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. Compared with the sh-NC OGD/R group, the sh-Cav1 OGD/R group exhibited significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly increased cell apoptotic rate, a significantly increased phosphorylation level of YAP1 at S127 site, and significantly decreased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. Compared with the sh-NC BHD group, the sh-Cav1 BHD group showed significantly decreased cell survival rate, cell migration rate, mesh number, node number, and lumen length, a significantly increased cell apoptotic rate, a significantly increased phosphorylation level of YAP1 at the S127 site, and significantly decreased nuclear displacement level of YAP1 and protein expression of HIF-1α, VEGF, and VEGFR2. YAP1 protein was present in the protein complex precipitated by the HIF-1α antibody, and HIF-1α protein was also present in the protein complex precipitated by the YAP1 antibody. The results confirmed that the drug-containing serum of BHD can increase the activity of YAP1/HIF-1α pathway in bEnd.3 cells damaged by OGD/R through Cav1 and promote angiogenesis in vitro.
Drugs, Chinese Herbal/pharmacology* ; Animals ; Mice ; Signal Transduction/drug effects* ; Glucose/metabolism* ; Caveolin 1/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; YAP-Signaling Proteins ; Oxygen/metabolism* ; Endothelial Cells/metabolism* ; Cell Line ; Adaptor Proteins, Signal Transducing/genetics* ; Neovascularization, Physiologic/drug effects* ; Cell Hypoxia/drug effects* ; Angiogenesis

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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To observe the effect of a Jianpi Huayu Jiedu formula on the NLRP3-mediated pyroptosis pathway in gastric precancerous lesion(GPL)associated with Helicobacter pylori(Hp)infection.Methods A GPL mouse model was prepared using Hp suspension gavage combined with Atp4a gene-deficient mice.The Jianpi Huayu Jiedu formula was administered as an intervention.Gastric mucosal tissue pathological damage was observed using hematoxylin and eosin(HE)staining.The presence of intestinal metaplasia(IM)was assessed using Alcian Blue-Periodic Acid Schiff(AB-PAS)staining.Ultrastructural changes in cell organelles were observed using transmission electron microscopy.Enzyme-linked immunosorbent assay(ELISA)was used to measure levels of gastrin-17(G-17),pepsinogen I(PGI),and proinflammatory cytokines IL-1β and IL-18.The expression of molecules related to the pyroptosis pathway was detected using Western blot and real-time quantitative PCR(RT-qPCR).Results Compared to the control group,Hp-related GPL mice exhibited gastric mucosal atrophy accompanied by IM and dysplasia.Damage to mitochondria and endoplasmic reticulum in parietal cells was observed.Levels of G-17,PGI,and proinflammatory cytokines IL-1β and IL-18 were elevated.The expression of molecules related to the pyroptosis pathway was increased.The Jianpi Huayu Jiedu formula significantly reduced gastric mucosal tissue pathological damage in GPL mice,decreased G-17 and PGI levels,mitigated inflammatory responses,and downregulated the expression of molecules related to the pyroptosis pathway.Conclusion The Jianpi Huayu Jiedu formula may exert its effects by inhibiting the NLRP3-mediated pyroptosis signaling pathway,thereby alleviating or even reversing the pathological damage of gastric mucosa in Hp-related GPL.

Objective To observe the effect of a Jianpi Huayu Jiedu formula on the NLRP3-mediated pyroptosis pathway in gastric precancerous lesion(GPL)associated with Helicobacter pylori(Hp)infection.Methods A GPL mouse model was prepared using Hp suspension gavage combined with Atp4a gene-deficient mice.The Jianpi Huayu Jiedu formula was administered as an intervention.Gastric mucosal tissue pathological damage was observed using hematoxylin and eosin(HE)staining.The presence of intestinal metaplasia(IM)was assessed using Alcian Blue-Periodic Acid Schiff(AB-PAS)staining.Ultrastructural changes in cell organelles were observed using transmission electron microscopy.Enzyme-linked immunosorbent assay(ELISA)was used to measure levels of gastrin-17(G-17),pepsinogen I(PGI),and proinflammatory cytokines IL-1β and IL-18.The expression of molecules related to the pyroptosis pathway was detected using Western blot and real-time quantitative PCR(RT-qPCR).Results Compared to the control group,Hp-related GPL mice exhibited gastric mucosal atrophy accompanied by IM and dysplasia.Damage to mitochondria and endoplasmic reticulum in parietal cells was observed.Levels of G-17,PGI,and proinflammatory cytokines IL-1β and IL-18 were elevated.The expression of molecules related to the pyroptosis pathway was increased.The Jianpi Huayu Jiedu formula significantly reduced gastric mucosal tissue pathological damage in GPL mice,decreased G-17 and PGI levels,mitigated inflammatory responses,and downregulated the expression of molecules related to the pyroptosis pathway.Conclusion The Jianpi Huayu Jiedu formula may exert its effects by inhibiting the NLRP3-mediated pyroptosis signaling pathway,thereby alleviating or even reversing the pathological damage of gastric mucosa in Hp-related GPL.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.