ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

[摘 要] 目的：探究重楼皂苷Ⅱ（PPⅡ）抑制甲状腺癌（TC）恶性生物学行为的分子机制。方法：常规培养甲状腺癌细胞TPC1，实验分为sh-NC、sh-可溶性半乳糖凝集素3（sh-LGALS3）、OE-NC、OE-LGALS3和 OE-LGALS3 + PPⅡ组，用转染试剂将应用质粒转染至各组TPC1细胞中。qPCR法检测TPC1细胞中LGALS3 mRNA的表达，WB法检测各组TPC1细胞中LGALS3、PI3K/AKT信号通路相关蛋白的表达，CCK-8法、Transwell实验、划痕愈合实验和流式细胞术分别检测各组TPC1细胞增殖、迁移和侵袭能力，以及细胞凋亡情况。结果：PPⅡ抑制TPC1细胞的增殖、迁移和侵袭，并诱导其凋亡（均P < 0.000 1）。数据库数据分析显示LGALS3在甲状腺癌组织中高表达（P < 0.001）且是PPⅡ的靶基因。LGALS1在TPC1细胞中呈高表达（P < 0.000 1），敲减LGALS3抑制TPC1细胞的恶性生物学行为，并促进其凋亡（均P < 0.000 1），PPⅡ通过抑制LGALS3 mRNA和蛋白的表达（P < 0.01或P < 0.001）从而抑制TPC1细胞的恶性生物学行为（P < 0.01或P < 0.000 1），PPⅡ抑制LGALS3表达抑制PI3K/AKT信号通路的激活水平（P <0.001或P <0.000 1），LGALS3通过PI3K/AKT信号通路促进TPC1细胞的恶性生物行为（P < 0.000 1）。结论：PPⅡ通过抑制TPC1细胞中LGALS3的表达，缓解PI3K/AKT信号通路的过度激活从而发挥抑癌作用。

OBJECTIVE To investigate the improvement effects and mechanism of Achyranthes bidentata total saponins （ABS） extract on vascular endothelial dysfunction in spontaneously hypertensive rat （SHR） based on cytochrome P450 4A （CYP4A）/20-hydroxyeicosatetetraenoic acid （20-HETE）/G protein-coupled receptor 75 （GPR75） axis. METHODS Ten Wistar- Kyoto rats were taken as the normal control group. Forty SHR were first stratified by systolic blood pressure and then， within each stratum， randomly assigned using a random-number table to the model group （MOD group）， captopril positive control group （CAP group， 10 mg/kg）， ABS low- and high-dose extract groups （ABS-L group， ABS-H group， 60 and 120 mg/kg）， with 10 rats in each group. Animals in each group were given the corresponding drug or equal volume of pure water by gavage， once a day， for 28 consecutive days. After the last administration， systolic blood pressure of rats was measured. The levels of vasoactive substances， inflammatory factors and oxidative stress indicators in serum were measured. The pathological changes of rat thoracic aorta were observed. The level of reactive oxygen species （ROS） in aortic tissue was analyzed. The expressions of endothelial nitric oxide synthase （eNOS）， CYP4A， GPR75， nuclear factor-κB p65 （NF-κB p65）， phosphorylated NF-κB p65， p22phox， and reduced nicotinamide adenine dinucleotide phosphate oxidase 4（NOX4） in thoracic aorta tissue were detected. RESULTS After 28 d of treatment， compared with MOD group， the systolic blood pressure of rats in the ABS-L and ABS-H groups decreased significantly. The levels of 20-HETE， angiotensin Ⅱ， interleukin-1β， interleukin-6， tumor necrosis factor-α， intercellular cell adhesion molecule-1 and malondialdehyde in serum were significantly reduced （P＜0.05 or P＜0.01）， while the levels of nitric oxide， superoxide dismutase， glutathione peroxidase and catalase were significantly increased （P＜0.05 or P＜0.01）. Intimal damage of thoracic aorta was reduced， and endothelial cell morphology was improved. The expressions of ROS， CYP4A， GPR75， p22phox， NOX4 and the phosphorylation level of NF-κB p65 protein in thoracic aorta were down-regulated or reduced （P＜0.05 or P＜0.01）， while the expression of eNOS was up-regulated （P＜0.05 or P＜0.01）. CONCLUSIONS ABS extract may alleviate the inflammatory response and oxidative stress in SHR effectively by down-regulating the expression of CYP4A， reducing the production of 20-HETE， inhibiting the activation of GPR75， and subsequently suppressing the activation of downstream NF-κB and NOX4， thereby improving hypertension-related vascular endothelial dysfunction.

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.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Based on commonly used acupoints in the clinical acupuncture treatment of functional dyspepsia （FD）， this study systematically analyzes the therapeutic differences and synergistic effects between local and distal point selection. It also examines the suitability of primary acupoint selection for different FD subtypes， postprandial distress syndrome （PDS） and epigastric pain syndrome （EPS）. The findings suggest that a combination of local and distal acupoints may be more appropriate as primary points for PDS， whereas local acupoints alone may be more suitable for EPS. Additionally， the study explores the impact of various factors， such as stimulation techniques， needling order， intensity or stimulation parameters， and depth， on the efficacy of acupuncture. It concludes that the intrinsic properties of acupoints are the primary determinants of therapeutic direction. Other factors mainly influence the magnitude rather than the direction of the effect. Future research may further investigate how different acupoint combinations， local versus distal， affect the treatment outcomes of FD subtypes， providing new insights for clinical acupuncture prescriptions.