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.

AIM To explore the clinical effects of Supplemented Buyang Huanwu Decoction on postoperative patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern.METHODS One hundred and twenty patients were randomly assigned into control group(60 cases)for 6-week intervention of conventional treatment,and observation group(60 cases)for 6-week intervention of both Supplemented Buyang Huanwu Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,spinal cord conduction signals(SEP amplitude,MEP amplitude),serum neurotrophic factors(NGF,IGF-1,BDNF),coagulation and inflammatory indices(PT,APTT,TNF-α,IL-1 β)and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,TNF-α,IL-1β(P＜0.05),increased spinal cord conduction signals,coagulation and inflammatory indices(P＜0.05),and shortened PT,APTT(P＜0.05),especially for the observation group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern,Supplemented Buyang Huanwu Decoction can safely and effectively promote neurological function recovery.

AIM To explore the clinical effects of Supplemented Buyang Huanwu Decoction on postoperative patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern.METHODS One hundred and twenty patients were randomly assigned into control group(60 cases)for 6-week intervention of conventional treatment,and observation group(60 cases)for 6-week intervention of both Supplemented Buyang Huanwu Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,spinal cord conduction signals(SEP amplitude,MEP amplitude),serum neurotrophic factors(NGF,IGF-1,BDNF),coagulation and inflammatory indices(PT,APTT,TNF-α,IL-1 β)and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,TNF-α,IL-1β(P＜0.05),increased spinal cord conduction signals,coagulation and inflammatory indices(P＜0.05),and shortened PT,APTT(P＜0.05),especially for the observation group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern,Supplemented Buyang Huanwu Decoction can safely and effectively promote neurological function recovery.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

Benign prostatic hyperplasia(BPH) is a common disease in middle-aged and elderly men, with lower urinary tract symptoms as the main manifestation, severely affecting the quality of life of patients. The pathogenesis of BPH is not yet fully understood, and there are still some challenges and limitations in western medicine treatment for BPH. Therefore, finding new and more effective treatment strategies is urgent. In recent years, many basic and clinical studies have confirmed the important role of traditional Chinese medicine in the treatment of BPH. This article reviews the progress of basic and clinical research in the treatment of BPH with traditional Chinese medicine, and believes that basic research mainly focuses on the active ingredients of Chinese medicine [regulating pathways such as NF-E2-related factor 2(Nrf2)/antioxidant response element(ARE), nuclear factor κB(NF-κB), epidermal growth factor receptor(EGFR)/signal transducer and activator of transcription 3(STAT3), phosphoinositide 3-kinase(PI3K)/protein kinase B(Akt)/mammalian target of rapamycin(mTOR), p38 mitogen-activated protein kinase(p38 MAPK)/forkhead box O subtype(FOXO3a), etc.], single Chinese herbs(regulating inflammatory factors, oxidative stress-related proteins, cell cycle-related proteins, and apoptotic factors, etc.), and Chinese herbal compounds and patent medicines [regulating extracellular signal-regulated kinase(ERK1/2), transforming growth factor-β(TGF-β)/Smad, mitogen-activated protein kinase(MAPK), PI3K/Akt, Nrf2, trefoil factor 2(TFF2)/Wnt, interleukin-6(IL-6)/Janus kinase 2(JAK2)/STAT3, hypoxia-inducible factor 1α(HIF-1α)/vascular endothelial growth factor receptor(VEGFR), etc.], and then play a therapeutic role by inhibiting BPH cell proliferation, oxidative stress, inflammatory response, promoting apoptosis, and inhibiting epithelial-mesenchymal transition. Clinical studies mainly focus on internal treatment, external treatment, combined internal and external treatment, and integrated Chinese and western medicine treatment as the main methods, aiming to improve traditional Chinese medicine syndrome scores, prostate symptom scores, residual urine volume, effective bladder volume, sexual quality of life, increase average urine flow rate, maximum urine flow rate, and promote balance of sex hormone secretion. Through this research, it is hoped to provide some reference ideas for clinical research and drug development for BPH.
Prostatic Hyperplasia/metabolism* ; Male ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Animals ; Signal Transduction/drug effects* ; Medicine, Chinese Traditional ; NF-E2-Related Factor 2/genetics*

Traditional Chinese medicine and its active ingredients have significant advantages in treating prostate cancer(PCa),and can complement the shortcomings of Western medicine,improving the quality of life for patients.This article reviews the research progress of traditional Chinese medicine and its effective ingredients in intervening in Wnt/β-catenin pathway to treat PCa,summarizing that the main effective ingredients in traditional Chinese medicine for preventing and treating PCa through this pathway are flavonoids,terpenes,alkaloids,phenols,and other compounds;the main traditional Chinese medicine formulas include Guben Qingyuanfang,Yishen Tongpang granules,etc,and discusses the mechanisms of action of these traditional Chinese medicines and their effective ingredients in intervening in this pathway to prevent and treat PCa,in order to provide a reference for the precise treatment of PCa and the application of traditional Chinese medicine research.

Traditional Chinese medicine and its active ingredients have significant advantages in treating prostate cancer(PCa),and can complement the shortcomings of Western medicine,improving the quality of life for patients.This article reviews the research progress of traditional Chinese medicine and its effective ingredients in intervening in Wnt/β-catenin pathway to treat PCa,summarizing that the main effective ingredients in traditional Chinese medicine for preventing and treating PCa through this pathway are flavonoids,terpenes,alkaloids,phenols,and other compounds;the main traditional Chinese medicine formulas include Guben Qingyuanfang,Yishen Tongpang granules,etc,and discusses the mechanisms of action of these traditional Chinese medicines and their effective ingredients in intervening in this pathway to prevent and treat PCa,in order to provide a reference for the precise treatment of PCa and the application of traditional Chinese medicine research.

As one kind of v-myb avian myeloblastosis viral oncogene homolog (MYB) transcription factors, R1-MYB (MYB-related) family plays an important role in plant growth and development, as well as environmental stress and hormone signal transduction. In this study, R1-MYB family genes in Rheum palmatum L. were systematically screened based on full-length transcriptome sequencing analysis. Firstly, the physicochemical, protein domain and molecular evolution characteristics of the coding proteins were analyzed. Furthermore, the tissue expression levels of R1-MYB genes were analyzed by RNA-seq. We also investigated the expression pattern of RpMYB24 in response to various hormones and abiotic stresses. The results showed that a total of 49 R1-MYB genes were identified, which mainly encoded thermally stable hydrophilic proteins. Most of the deduced proteins were predicted to locate in nucleus. Each protein had a large proportion of random curl and α helix, and also had the W-type conserved amino acids which were the signature of MYB. R1-MYB family members were distributed in five subgroups, including circadian clock associated 1 (CCA1)-like, I-box (GATAAG)-like, CAPRICE (CPC)-like, telomere repeat binding factor (TRF)-like and TATA binding protein (TBP)-like, and the number of CCA1-like was the majority. RNA-seq revealed that 49 R1-MYB genes were differentially expressed in roots, rhizomes and leaves of R. palmatum, and the expression levels of 15 and 23 genes in roots and rhizomes were higher than those in leaves, respectively. RpMYB24 transcript was induced by abscisic acid (ABA), salicylic acid (SA), and methyl jasmonate (MeJA) treatment, and could also significantly respond to injury, low temperature and high temperature stresses except drought stress. This study systematically identified the R1-MYB family genes and their molecular characteristics, better for further gene functional validation, and then provide a scientific basis for the transcriptional regulation mechanism research into rhubarb quality formation.