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

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

Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

Objective To investigate the efficacy of varying doses of methylprednisolone(MP)on mice with acute exacerbations of chronic obstructive pulmonary disease(AECOPD)induced with influenza A virus(IAV).Methods Mouse model of COPD was established using LPS combined with smoking for 12 weeks,and then these COPD mice were treated with administration of 40 μL IAV via nasal drip to establish a AECOPD model.A total of 15 AECOPD mice were randomly divided into low-,medium-and high-dose MP groups,oseltamivir group and blank group.The body weight and survival time were monitored within 10 d after IAV infection.On days 1,3,and 5 post-treatment,lung function was assessed using whole-body plethysmography(WBP),inflammatory factors in bronchoalveolar lavage fluid(BALF)were quantified with ELISA,viral titers in BALF were determined using plaque assays,and colony-forming units were evaluated with blood agar plates.Immunofluorescence analysis:① Pulmonary immunofluorescence assay:Mice were randomly categorized into(n=4):LPS 1-day group,LPS 3-day group,and LPS+MP treatment group.All groups received an initial dose of LPS via atomization;subsequently,the LPS+MP treatment group received a single gavage dose of MP.Lung tissues were harvested from the 1-day LPS group on 1 d post-treatment,and from the 3-day LPS and LPS+MP groups on 3 d for immunofluorescence staining.② Cellular immunofluorescence assay:Mouse bone marrow neutrophils were classified into blank control(no intervention),LPS stimulation(LPS group),MP intervention with LPS stimulation(LPS+MP group),and MP intervention alone(MP group).The above cells were collected in 4 h after corresponding interventions for subsequent cellular immunofluorescence analysis.Results ①The medium-dose MP group demonstrated the most significant improvement in survival rate,weight recovery,and lung function when compared to other groups(P＜0.05).② Treatment of medium-dose MP obviously reduced the levels of IL-6 and neutrophil extracellular traps(NETs)(P＜0.05),while,elevated inflammatory factors and NETs were observed in the high-dose MP group on day 5 post-treatment.③ Notable decline in the lung injury score was found in the medium-dose MP group than the other groups(P＜0.05).④The high-dose MP group exhibited substantial bacterial proliferation and delayed viral clearance since day 5 after treatment.Conclusion Medium-dose MP shows best efficacy in treatment of IAV-induced AECOPD,and the dose neither delays viral clearance nor increases the risk of bacterial infection following viral infection.

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

OBJECTIVES: To investigate the therapeutic mechanism of 2,6-dimethoxy-1,4-benzoquinone (DMQ) for alleviating dextran sulfate sodium (DSS)-induced ulcerative colitis (UC) in mice. METHODS: Eighteen male C57BL/6J mice were equally randomized into control group, DSS group and DMQ treatment group. In DSS and DMQ groups, the mice were treated with DSS in drinking water to induce UC, and received intraperitoneal injections of sterile PBS or DMQ (20 mg/kg) during modeling. The changes in body weight, disease activity index (DAI), colon length, spleen weight, and colon histological scores of the mice were examined, and the percentages of Th17 and IFN-γ⁺ CD8⁺ T cells in the mesenteric lymph nodes and spleen were analyzed using flow cytometry. The expressions of tight junction proteins (Occludin and ZO-1), proteins associated with inflammasome activation (caspase-1 and p20), IL-1β and TNF-α in the colon tissues were detected using Western blotting or ELISA. In the cell experiment, mouse bone marrow-derived macrophages (BMDMs) primed with lipopolysaccharide (LPS) were treated with DMQ, followed by stmulation with nigericin to activate the classical NLRP3 inflammasome pathway. In cultured human peripheral blood mononuclear cells (PBMCs) treated with either LPS alone or LPS plus nigericin, the effects of DMQ on inflammasome activation, pyroptosis, and cytokine release were evaluated via Western blotting, ELISA, and flow cytometry. RESULTS: In DSS-treated mice, DMQ treatment significantly alleviated DSS-induced body weight loss, colon shortening, spleen enlargement, and colon inflammation. The DMQ-treated mice showed significantly reduced percentages of Th17 cells and IFN-γ⁺ CD8⁺ T cells in the mesenteric lymph nodes and spleen, with increased occludin and ZO-1 expressions and decreased caspase-1 expression in the colon tissue. DMQ obviously inhibited classical NLRP3 inflammasome activation in mouse BMDMs and both the classical and alternative pathways of NLRP3 activation in human PBMCs, causing also suppression of caspase-1-dependent pyroptosis. CONCLUSIONS DMQ ameliorates DSS-induced UC in mice by inhibiting NLRP3 inflammasome activation.
Animals ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Mice, Inbred C57BL ; Colitis, Ulcerative/metabolism* ; Dextran Sulfate/adverse effects* ; Male ; Inflammasomes/metabolism* ; Mice ; Benzoquinones/therapeutic use* ; Th17 Cells ; Caspase 1/metabolism*

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

ObjectiveTo explore the effect of modified Lianpoyin formula （LPYJWF） in the treatment of Helicobacter pylori （Hp）-associated gastric mucosal damage based on mitochondrial autophagy and NLRP3 inflammasome signaling pathway. MethodsA total of 60 eight-week-old Balb/c male mice were assigned via the random number table method into control， model， high-dose LPYJWF （LPYJWF-H， 27.3 g·kg^-1·d^-1）， medium-dose LPYJWF （LPYJWF-M， 13.65 g·kg^-1·d^-1）， low-dose LPYJWF （LPYJWF-L， 6.83 g·kg^-1·d^-1）， and quadruple therapy groups. Except the control group， other groups were modeled for Hp infection. Mice were administrated with LPYJWF at corresponding doses by gavage. Quadruple therapy group was given omeprazole （6.06 mg·kg^-1·d^-1） + amoxicillin （303 mg·kg^-1·d^-1） + clarithromycin （151.67 mg·kg^-1·d^-1） + colloidal pectin capsules （30.3 mg·kg^-1·d^-1） by gavage. The control group was given an equal volume of 0.9% NaCl for 14 days. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of gastric mucosa， and Warthin-Starry （W-S） silver staining was used to detect Hp colonization. Transmission electron microscopy was employed to observe the mitochondrial ultrastructure of the gastric tissue， and immunofluorescence co-localization assay was adopted to detect the expression of mitochondrial transcription factor A （TFAM） and translocase of the outer mitochondrial membrane member 20 （TOMM20）. The water-soluble tetrazolium salt method and thiobarbituric acid method were used to determine the levels of superoxide dismutase （SOD） and malondialdehyde （MDA）， respectively， in the gastric tissue. Western blot was employed to measure the protein levels of PTEN-induced kinase 1 （PINK1）， Parkin， p62， microtubule-associated protein 1 light chain 3 （LC3）， NOD-like receptor protein 3 （NLRP3）， apoptosis-associated speck-like protein containing a CARD （ASC）， interleukin-1β （IL-1β）， and interleukin-18 （IL-18）. Real-time quantitative PCR was employed to assess the mRNA levels of PINK1， Parkin， p62， and LC3. ResultsCompared with the control group， the model group presented obvious gastric mucosal damage， colonization of a large number of Hp， severe mitochondrial damage， vacuolated structures due to excessive autophagy， reduced TOMM20 and TFAM co-expression in the gastric mucosal tissue， and reduced SOD and increased MDA （P<0.01）. In addition， the gastric tissue in the model group showed up-regulated protein and mRNA levels of PINK1， Parkin， and LC3 and down-regulated protein and mRNA levels of p62 （P<0.01， as well as increased expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 （P<0.01）. Compared with the model group， the LPYJWF and quadruple therapy groups showed alleviated pathological damage of gastric mucosa， reduced Hp colonization， mitigated mitochondrial damage， and increased co-expression of TOMM20 and TFAM. The SOD level was elevated in the LPYJWF-L group （P<0.01）， and the MDA levels became lowered in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. Furthermore， the LPYJWF and quadruple therapy groups showed down-regulated mRNA levels of PINK1， Parkin， and LC3 and protein levels of PINK1 and Parkin， and up-regulated mRNA level of p62 （P<0.01）. The LPYJWF-M， LPYJWF-H， and quadruple therapy groups showcased down-regulated LC3 Ⅱ/LC3 Ⅰ level （P<0.05， P<0.01） and up-regulated protein level of p62 （P<0.01）. The expression of inflammasome-associated proteins NLRP3， ASC， IL-1β， and IL-18 were reduced in the LPYJWF and quadruple therapy groups （P<0.05， P<0.01）. ConclusionLPYJWF ameliorates gastric mucosal damage and exerts mucosa-protective effects in Hp-infected mice， which may be related to the inhibition of excessive mitochondrial autophagy， thereby inhibiting the activation of the NLRP3 inflammasome pathway.