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.

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.

This study aims to investigate the mechanism by which resveratrol(RES) alleviates cerebral vascular endothelial damage in sepsis-associated encephalopathy(SAE) through network pharmacology and animal experiments. By using network pharmacology, the study identified common targets and genes associated with RES and SAE and constructed a protein-protein interaction( PPI) network. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed to pinpoint key signaling pathways, followed by molecular docking validation. In the animal experiments, a cecum ligation and puncture(CLP) method was employed to induce SAE in mice. The mice were randomly assigned to the sham group, CLP group, and medium-dose and high-dose groups of RES. The sham group underwent open surgery without CLP, and the CLP group received an intraperitoneal injection of 0. 9% sodium chloride solution after surgery. The medium-dose and high-dose groups of RES were injected intraperitoneally with 40 mg·kg-1 and 60 mg·kg~(-1) of RES after modeling, respectively, and samples were collected 12 hours later. Neurological function scores were assessed, and the wet-dry weight ratio of brain tissue was detected. Serum superoxide dismutase(SOD), catalase( CAT) activity, and malondialdehyde( MDA) content were measured by oxidative stress kit. Histopathological changes in brain tissue were examined using hematoxylin-eosin(HE) staining. Transmission electron microscopy was employed to evaluate tight cell junctions and mitochondrial ultrastructure changes in cerebral vascular endothelium. Western blot analysis was performed to detect the expression of zonula occludens1( ZO-1), occludin, claudins-5, optic atrophy 1( OPA1), mitofusin 2(Mfn2), dynamin-related protein 1(Drp1), fission 1(Fis1), and hypoxia-inducible factor-1α(HIF-1α). Network pharmacology identified 76 intersecting targets for RES and SAE, with the top five core targets being EGFR, PTGS2, ESR1, HIF-1α, and APP. GO enrichment analysis showed that RES participated in the SAE mechanism through oxidative stress reaction. KEGG enrichment analysis indicated that RES participated in SAE therapy through HIF-1α, Rap1, and other signaling pathways. Molecular docking results showed favorable docking activity between RES and key targets such as HIF-1α. Animal experiment results demonstrated that compared to the sham group, the CLP group exhibited reduced nervous reflexes, decreased water content in brain tissue, as well as serum SOD and CAT activity, and increased MDA content. In addition, the CLP group exhibited disrupted tight junctions in cerebral vascular endothelium and abnormal mitochondrial morphology. The protein expression levels of Drp1, Fis1, and HIF-1α in brain tissue were increased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were decreased. In contrast, the medium-dose and high-dose groups of RES showed improved neurological function, increased water content in brain tissue and SOD and CAT activity, and decreased MDA content. Cell morphology in brain tissue, tight junctions between endothelial cells, and mitochondrial structure were improved. The protein expressions of Drp1, Fis1, and HIF-1α were decreased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were increased. This study suggested that RES could ameliorate cerebrovascular endothelial barrier function and maintain mitochondrial homeostasis by inhibiting oxidative stress after SAE damage, potentially through modulation of the HIF-1α signaling pathway.
Animals ; Mice ; Network Pharmacology ; Resveratrol/administration & dosage* ; Male ; Sepsis-Associated Encephalopathy/genetics* ; Signal Transduction/drug effects* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Endothelium, Vascular/metabolism* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Humans ; Sepsis/complications* ; Oxidative Stress/drug effects*

The chemical constituents of Commiphora myrrha was investigated by column chromatography on silica gel, ODS, Sephadex LH-20, and semi-preparative HPLC. Their structures were elucidated by comprehensive spectroscopic methods including UV, IR, MS, NMR, as well as ECD calculation. Seven compounds were isolated from the dichloromethane-soluble fraction of C. myrrha and their structures were identified as(1S,2R,4S,5R,8S)-guaiane-2-hydroxy-7(11),10(15)-dien-6-oxo-12,8-olide(1), commipholide E(2), myrrhterpenoid H(3), myrrhterpenoid I(4), myrrhterpenoid E(5), 2α-methoxy-8α-hydroxy-6-oxogermacra-1(10),7(11)-dien-8,12-olide(6), 8,12-epoxy-1α,9α-hydroxy-eudesma-7,11-diene-6-dione(7). Compound 1 was a new compound and named myrrhterpenoid P. Compound 7 was isolated from Commiphora genus for the first time. Compounds 2, 5, and 6 significantly inhibited nitric oxide(NO) production in LPS-stimulated RAW264.7 cells, with IC_(50) values of(49.67±4.16),(40.80±1.27),(47.22±0.87) μmol·L~(-1), respectively [indomethacin as the positive control, with IC_(50) value of(63.92±2.60) μmol·L~(-1)].
Commiphora/chemistry* ; Animals ; Mice ; Resins, Plant/chemistry* ; Sesquiterpenes/isolation & purification* ; Molecular Structure ; Nitric Oxide ; Macrophages/metabolism* ; RAW 264.7 Cells ; Drugs, Chinese Herbal/pharmacology*

This study aims to reveal the correlation between the pharmacokinetics(PK) and pharmacodynamics(PD) of multiple components in Epimedii Folium-Chuanxiong Rhizoma and clarify the pharmacodynamic material basis and mechanism of this herb pair in treating osteoarthritis. The Hulth method was used to establish the rat model of osteoarthritis and plasma was collected at various time points after drug administration. The plasma concentrations of multiple components were measured. Enzyme-linked immunosorbent assay(ELISA) was used to measure the plasma concentrations of matrix metalloproteinase(MMP)-3, MMP-13, interleukin-1β(IL-1β), nitric oxide(NO), and tumor necrosis factor-α(TNF-α) as pharmacodynamic indicators. Self-defined weighting coefficients were used to calculate the PK and PD data, and a Sigmoid E_(max) fitting model was used to evaluate the synergistic effect of the compatibility of Epimedii Folium-Chuanxiong Rhizoma. The PK-PD models for Epimedii Folium, Chuanxiong Rhizoma, and Epimedii Folium-Chuanxiong Rhizoma were E=(1.926×C~(2.652))/(0.136 6~(2.652)+C~(2.652)), E=(1.618×C~(345.2))/(0.118 4~(345.2)+C~(345.2)), and E=(2.305×C~(2.786))/(0.240 3~(2.786)+C~(2.786)), respectively. The E_(max) of Epimedii Folium-Chuanxiong Rhizoma was larger than those of the two herbal medicines alone. The EC_(50) of the herb pair was lower than the sum of Epimedii Folium and Chuanxiong Rhizoma alone. The concentrations of MMP-3, MMP-13, IL-1β, NO, and TNF-α were correlated with mass concentrations of multiple components in Epimedii Folium and Chuanxiong Rhizoma, and the compatibility was better than single use. Epimedii Folium, Chuanxiong Rhizoma, and Epimedii Folium-Chuanxiong Rhizoma may play a role in the treatment of osteoarthritis by inhibiting MMP-3, MMP-13, IL-1β, NO, and TNF-α.
Animals ; Rats ; Drugs, Chinese Herbal/pharmacology* ; Male ; Rats, Sprague-Dawley ; Osteoarthritis/metabolism* ; Epimedium/chemistry* ; Interleukin-1beta/blood* ; Tumor Necrosis Factor-alpha/blood* ; Disease Models, Animal ; Nitric Oxide/blood* ; Humans ; Rhizome/chemistry*

ObjectiveTo study the regulatory effect of the partial sequence within the 3’ untranslated region （3’UTR） of slit guidance ligand 1 （Slit1） （Slit1-3’UTR） on the fibrotic phenotypes of cardiac fibroblasts （CFs） and its potential mechanism. MethodsThe adenovirus vector was used to overexpress the 1526nt sequence of Slit1-3’UTR in ICR neonatal mouse CFs （mCFs）. The expression of fibrosis-related genes in mCFs， such as collagen type 1 alpha1（COL1A1）， collagen type 3 alpha3 （COL3A1） and alpha smooth muscle actin （α-SMA） were detected by Western blot assay. The effect of Slit1-3’UTR 1526nt on the proliferation and migration of mCFs was assessed by EdU staining and Trans-well assays. Angiotensin Ⅱ （Ang Ⅱ） was used to treat mCFs， and the impact of Slit1-3’UTR 1526nt on the fibrotic phenotypes of Ang Ⅱ-induced mCFs was evaluated. After overexpression of Slit1-3’UTR 1526nt， miR-34a-5p mimic was transfected into mCFs， followed by actinomycin D treatment to detect the mRNA stability of Slit1-3’UTR 1526nt， and the levels of miR-34a-5p and its target gene SIRT1（si-SIRT1） in mCFs were determined. The effects of miR-34a-5p and small interfering RNA targeting SIRT1 on the Slit1-3’UTR 1526nt-mediated regulation of fibrotic phenotypes were also determined. ResultsAdenovirus-mediated overexpression of Slit 1-3’UTR 1526nt was achieved in mCFs. Overexpression of Slit 1-3’UTR 1526nt markedly inhibited the expression of the fibrosis-related genes， proliferation and migration of mCFs and fibrotic phenotypes of Ang Ⅱ. The results of actinomycin D assay showed that miR-34a-5p inhibited the stability of Slit1-3’UTR 1526nt in mCFs， while the level of miR-34a-5p was reduced in mCFs with overexpression of Slit1-3’UTR 1526nt. Transfection of miR-34a-5p promoted the fibrotic phenotypes， and reversed the inhibitory effect of Slit1-3’UTR 1526nt on the fibrotic phenotypes of mCFs. Overexpression of Slit1-3’UTR 1526nt significantly increased the level of miR-34a-5p target gene SIRT1 in mCFs. Transfection of miR-34a-5p and si-SIRT1 consistently reversed the inhibitory effects of Slit1-3’UTR 1526nt on the fibrotic phenotypes of mCFs. ConclusionSlit1-3’UTR1526nt inhibits the fibrotic phenotypes of mCFs by binding to miR-34a-5p and increasing the expression of its target gene of SIRT1.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

PURPOSE: To investigate the protective effect of sub-hypothermic mechanical perfusion combined with membrane lung oxygenation on ischemic hypoxic injury of yorkshire brain tissue caused by traumatic blood loss. METHODS: This article performed a random controlled trial. Brain tissue of 7 yorkshire was selected and divided into the sub-low temperature anterograde machine perfusion group (n = 4) and the blank control group (n = 3) using the random number table method. A yorkshire model of brain tissue injury induced by traumatic blood loss was established. Firstly, the perfusion temperature and blood oxygen saturation were monitored in real-time during the perfusion process. The number of red blood cells, hemoglobin content, NA⁺, K⁺, and Ca²⁺ ions concentrations and pH of the perfusate were detected. Following perfusion, we specifically examined the parietal lobe to assess its water content. The prefrontal cortex and hippocampus were then dissected for histological evaluation, allowing us to investigate potential regional differences in tissue injury. The blank control group was sampled directly before perfusion. All statistical analyses and graphs were performed using GraphPad Prism 8.0 Student t-test. All tests were two-sided, and p value of less than 0.05 was considered to indicate statistical significance. RESULTS: The contents of red blood cells and hemoglobin during perfusion were maintained at normal levels but more red blood cells were destroyed 3 h after the perfusion. The blood oxygen saturation of the perfusion group was maintained at 95% - 98%. NA⁺ and K⁺ concentrations were normal most of the time during perfusion but increased significantly at about 4 h. The Ca²⁺ concentration remained within the normal range at each period. Glucose levels were slightly higher than the baseline level. The pH of the perfusion solution was slightly lower at the beginning of perfusion, and then gradually increased to the normal level. The water content of brain tissue in the sub-low and docile perfusion group was 78.95% ± 0.39%, which was significantly higher than that in the control group (75.27% ± 0.55%, t = 10.49, p < 0.001), and the difference was statistically significant. Compared with the blank control group, the structure and morphology of pyramidal neurons in the prefrontal cortex and CA1 region of the hippocampal gyrus were similar, and their integrity was better. The structural integrity of granulosa neurons was destroyed and cell edema increased in the perfusion group compared with the blank control group. Immunofluorescence staining for glail fibrillary acidic protein and Iba1, markers of glial cells, revealed well-preserved cell structures in the perfusion group. While there were indications of abnormal cellular activity, the analysis showed no significant difference in axon thickness or integrity compared to the 1-h blank control group. CONCLUSIONS Mild hypothermic machine perfusion can improve ischemia and hypoxia injury of yorkshire brain tissue caused by traumatic blood loss and delay the necrosis and apoptosis of yorkshire brain tissue by continuous oxygen supply, maintaining ion homeostasis and reducing tissue metabolism level.
Animals ; Perfusion/methods* ; Disease Models, Animal ; Brain Injuries/etiology* ; Swine ; Male ; Hypothermia, Induced/methods*

OBJECTIVES: To investigate the association between insulin resistance and uterine volume in girls with idiopathic central precocious puberty (ICPP). METHODS: A retrospective study was conducted involving 61 girls diagnosed with ICPP who visited the pediatric growth and development clinic of the Third Affiliated Hospital of Zhengzhou University between January 2022 and September 2024, designated as the ICPP group, and 61 normally developing girls as the control group. The differences in insulin resistance index (homeostasis model assessment of insulin resistance, HOMA-IR), uterine volume, and other indicators between the two groups were compared, and the relationship between insulin resistance and uterine volume in these girls was analyzed. RESULTS: The uterine volume and HOMA-IR level in the ICPP group were significantly higher than those in the control group (P<0.05). Correlation analysis revealed that there was a positive correlation between HOMA-IR level and uterine volume in the ICPP group (r_s=0.643, P<0.001). Multiple linear regression analysis indicated that as HOMA-IR increased,uterine volume in the girls tended to increase (P<0.05). CONCLUSIONS There is an association between insulin resistance and uterine volume in girls with ICPP, and as HOMA-IR increases, uterine volume in the girls also increases.
Humans ; Female ; Insulin Resistance ; Puberty, Precocious/metabolism* ; Uterus/pathology* ; Child ; Retrospective Studies ; Organ Size ; Linear Models