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.

Bismuth iodide(BiOI)with different crystal plane ratios of(110)to(001)was synthesized,and typeⅡheterojunction formed between(001)and(110)crystal planes of BiOI was used to improve the separation efficiency of photogenerated electrons and holes.Then the BiOI(001)/(110)was composited with nitrogen-doped graphene quantum dots(N-GQDs)to prepare a ternary composites,which could enhance the range and intensity of light absorption,and prolonged the lifetime of photogenerated electrons due to the formation of Z-scheme heterojunctions between BiOI and N-GQDs,thereby leading to the excellent photoelectric performance of the BiOI/N-GQDs for generating sensitive photoelectric response signals.A photoelectrochemical sensor for sensitive detection of chlorpyrifos(CPF)was designed with BiOI/N-GQDs-modified FTO electrode as a photocathode.The S and N atoms contained in CPF were coordinated with Bi(Ⅲ)on the surface of BiOI,which reduced the photocurrent of BiOI/N-GQDs.The photocurrent change was linear with logarithm of concentration of CPF in the range of 1.5×10-12-5.0×10-9 mol/L,and the detection limit was 1.5×10-12 mol/L.The sensor was highly sensitive,selective and stable,and could be used for determination of trace CPF in environmental and food samples.

Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.

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*

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

This study elucidates the mechanism of Rhodiolae Crenulatae Radix et Rhizoma(RCRR) in protecting brain microvascular endothelial cells from oxygen-glucose deprivation(OGD) injury and reveals the modern pharmacological mechanism of RCRR's traditional use in nourishing Qi and promoting blood circulation to protect endothelial cells. The scratch assay was employed to assess the migratory capacity of endothelial cells. Immunofluorescence and Western blot techniques were employed to assess the protein expression of tight junction proteins zonula occludens-1(ZO-1), occludin, claudin-5, and proteins of the phosphoinositide 3-kinase(PI3K)/protein kinase B(AKT)/glycogen synthase kinase-3beta(GSK3β) pathway. The results demonstrated that 63 bioactive components and 125 potential core targets of RCRR were identified from the ETCM, TCMBank, and SwissTargetPrediction databases, as well as from the literature. A total of 1 708 brain microvascular endothelial cell-related targets were identified from the GeneCards and OMIM databases, and 52 targets were obtained by intersecting drug components with cell targets. The protein-protein interaction(PPI) network analysis revealed that AKT1, epidermal growth factor receptor(EGFR), matrix metalloproteinase 9(MMP9), estrogen receptor 1(ESR1), proto-oncogene tyrosine-protein kinase(SRC), peroxisome proliferator-activated receptor gamma(PPARG), GSK3β, and matrix metalloproteinase 2(MMP2) were considered hub genes. The KEGG enrichment analysis identified the PI3K/AKT pathway as the primary signaling pathway. Cell experiments demonstrated that RCRR-containing serum could enhance the migratory capacity of brain microvascular endothelial cells and the expression of tight junction proteins following OGD injury, which may be associated with the downregulation of the PI3K/AKT/GSK3β pathway. This study elucidates the pharmacological mechanism of RCRR in protecting brain microvascular endothelial cells through network pharmacology, characterized by multiple components and targets. These findings were validated through in vitro experiments and provide important ideas and references for further research into the molecular mechanisms of RCRR in protecting brain microvascular endothelial cells.
Endothelial Cells/cytology* ; Glycogen Synthase Kinase 3 beta/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Phosphatidylinositol 3-Kinases/genetics* ; Signal Transduction/drug effects* ; Brain/metabolism* ; Humans ; Animals ; Rhizome/chemistry* ; Microvessels/metabolism* ; Brain Ischemia/drug therapy*

The chemical components of Carthami Flos were investigated by using macroporous resin, silica gel column chromatography, reversed-phase octadecylsilane(ODS) column chromatography, Sephadex LH-20, and semi-preparative high-performance liquid chromatography(HPLC). The planar structures of the compounds were established based on their physicochemical properties and ultraviolet-visible(UV-Vis), infrared(IR), high-resolution electrospray ionization mass spectrometry(HR-ESI-MS), and nuclear magnetic resonance(NMR) spectroscopic technology. The absolute configurations were determined by comparing the calculated and experimental electronic circular dichroism(ECD). Six flavonoid C-glycosides were isolated from the 30% ethanol elution fraction of macroporous resin obtained from the 95% ethanol extract of Carthami Flos, and identified as saffloquinoside F(1), 5-hydroxysaffloneoside(2), iso-5-hydroxysaffloneoside(3), isosafflomin C(4), safflomin C(5), and vicenin 2(6). Among these, the compounds 1 to 3 were new chalcone C-glycosides. The compounds 1, 2, 4, and 5 could significantly increase the viability of H9c2 cardiomyocytes damaged by oxygen-glucose deprivation/reoxygenation(OGD/R) at a concentration of 50 μmol·L~(-1), showing their good cardioprotective activity.
Glycosides/pharmacology* ; Flowers/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Carthamus tinctorius/chemistry* ; Chalcones/pharmacology* ; Animals

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) cause a severe neurodevelopmental disorder, yet the impact of truncating mutations remains unclear. Here, we introduce the Cdkl5^492stop mouse model, mimicking C-terminal truncating mutations in patients. 492stop/Y mice exhibit altered dendritic spine morphology and spontaneous seizure-like behaviors, alongside other behavioral deficits. After creating cell lines with various Cdkl5 truncating mutations, we found that these mutations are regulated by the nonsense-mediated RNA decay pathway. Most truncating mutations result in CDKL5 protein loss, leading to multiple disease phenotypes, and offering new insights into the pathogenesis of CDKL5 disorder.
Animals ; Disease Models, Animal ; Mice ; Protein Serine-Threonine Kinases/deficiency* ; Mutation/genetics* ; Epileptic Syndromes/genetics* ; Humans ; Dendritic Spines/pathology* ; Spasms, Infantile/genetics* ; Male ; Seizures/genetics* ; Mice, Inbred C57BL

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.