Hepatitis B virus （HBV） infection is the primary cause of viral hepatitis and represents a substantial disease burden in China. However， effective and safe agents capable of completely eliminating HBV DNA are still lacking. In modern medicine， anti-HBV strategies mainly target covalently closed circular DNA （cccDNA）， among other mechanisms， and multiple novel drugs are currently under clinical investigation. Traditional medicine has been shown to exert anti-HBV effects through direct pathways， such as blocking viral entry， as well as indirect pathways， including the regulation of programmed cell death. Studies have confirmed that the integration of traditional Chinese medicine （TCM） and Western medicine in treating HBV infection and its related complications offers complementary advantages， particularly in enhancing HBV clearance rates， improving liver function， preventing various complications， and delaying the progression from hepatic fibrosis to hepatocellular carcinoma. This review focuses on advances in anti-HBV research involving TCM， Western medicine， and their integrated application， aiming to provide a basis for integrated HBV therapy and new drug development.

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 explore the technical process and clinical application of laparoscopic combined upper midline incision minimally invasive liver transplantation. Methods A retrospective analysis was conducted on 30 cases of laparoscopic combined upper midline incision minimally invasive liver transplantation. The cases were divided into cirrhosis group (15 cases) and liver failure group (15 cases) based on the primary disease. The surgical and postoperative conditions of the two groups were compared. Results All patients successfully underwent laparoscopic "clockwise" liver resection, with no cases of passive conversion to open surgery or intolerance to pneumoperitoneum. In 6 cases, the right lobe was relatively large, and the right hepatic ligaments could not be completely mobilized. One case required an additional reverse "L" incision during open surgery. All patients successfully completed the liver transplantation, with no major intraoperative bleeding, cardiovascular events, or other occurrences in the 30 patients. The model for end-stage liver disease (MELD) score in the cirrhosis group was lower than that in the liver failure group (P<0.001). There were no statistically significant differences between the two groups in terms of age, surgical time, blood loss, anhepatic phase, or cold ischemia time (all P>0.05). During the perioperative period, there was 1 case of hepatic artery embolism, 1 case of portal vein anastomotic stenosis, no complications of hepatic vein and inferior vena cava, and 3 cases of biliary anastomotic stenosis, all of which occurred in the liver failure group. Conclusions In strictly selected cases, the minimally invasive liver transplantation technique combining laparoscopic hepatectomy with upper midline incision for graft implantation has the advantages of smaller incisions, less bleeding, relatively easier operation, and faster postoperative recovery, which is worthy of clinical promotion and application.

BACKGROUND:Computer-assisted implant surgery can improve implantation accuracy,but the use of implant template in the posterior tooth area is limited for patients with small opening and small interocclusal distance.Therefore,the digital guide has been improved. OBJECTIVE:To study the effect of modified implant template on the accuracy of assisted implantation in missing second molars. METHODS:From July 2020 to July 2023,40 patients who received digital guide plate implantation or free hand implantation to repair missing second molars were selected from First Affiliated Hospital of Guangzhou Medical University.According to the coin toss method,patients were randomly divided into a trial group(n=22;modified digital guide assisted implantation)and a control group(n=18;free hand implantation).The data of neck deviation,tip deviation,depth deviation,and angle deviation were compared between groups for preoperative and postoperative cone beam CT overlap analysis.One week after the operation,the patients'satisfaction with the operation was assessed by visual analog scale score. RESULTS AND CONCLUSION:(1)The trial group included 25 implants(12 in the upper jaw and 13 in the lower jaw);the control group included 23 implants(8 in the upper jaw and 15 in the lower jaw).The neck deviation,tip deviation,depth deviation,and angle deviation of the trial group were all smaller than those of the control group(P<0.05,P<0.001).There was no significant difference in accuracy between the maxillary and mandibular implant site in the trial group(P>0.05).(2)There was no significant difference in satisfaction with the operation between the two groups(P>0.05).(3)The results showed that improving the digital guide plate for assisted implantation for missing second molar can improve surgical accuracy and is suitable for patients with small opening and small interocclusal distance in the posterior tooth area.

Hepatic fibrosis is a chronic pathological condition characterized by hepatic stellate cell activation and excessive deposition of extracellular matrix， which would progress to liver cirrhosis and even hepatocellular carcinoma. Therefore， reversal of hepatic fibrosis is of great importance for improving quality of life and prolonging survival time. Currently， various therapeutic drugs for hepatic fibrosis have entered the stage of clinical trial. This article reviews the research advances in therapeutic drugs for hepatic fibrosis in the recent years， in order to provide insights into the treatment of hepatic fibrosis and future research directions for drugs.

Intestinal fibrosis is a significant clinical challenge in inflammatory bowel diseases, but no effective anti-fibrotic therapy is currently available. Glucagon receptor (GCGR) and glucagon-like peptide 1 receptor (GLP1R) are both peptide hormone receptors involved in energy metabolism of epithelial cells. However, their role in intestinal fibrosis and the underlying mechanisms remain largely unexplored. Herein GCGR and GLP1R were found to be reduced in the stenotic ileum of patients with Crohn's disease as well as in the fibrotic colon of mice with chronic colitis. The downregulation of GCGR and GLP1R led to the accumulation of the metabolic byproduct lactate, resulting in histone H3K9 lactylation and exacerbated intestinal fibrosis through epithelial-to-mesenchymal transition (EMT). Dual activating GCGR and GLP1R by peptide 1907B reduced the H3K9 lactylation in epithelial cells and ameliorated intestinal fibrosis in vivo. We uncovered the role of GCGR/GLP1R in regulating EMT involved in intestinal fibrosis via histone lactylation. Simultaneously activating GCGR/GLP1R with the novel dual agonist peptide 1907B holds promise as a treatment strategy for alleviating intestinal fibrosis.

Objective To clone and express the heat shock cognate protein 20 (SjHsc20) of Schistosoma japonicum, and to preliminarily investigate its biological characteristics. Methods The target fragment of the SjHsc20 gene was amplified using PCR assay and cloned into the pET-28a(+) expression plasmid to generate the recombinant expression vector pET-28a(+)-SjH-sc20, which was then transformed into Escherichia coli BL21 (DE3) competent cells. The recombinant SjHsc20 (rSjHsc20) protein was induced with isopropyl β-D-thiogalactopyranoside (IPTG) and purified, and the expression of the rSjHsc20 protein was checked with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The immunogenicity of the rSjHsc20 protein was detected using Western blotting, and the transcriptional levels of SjHsc20 were quantified in S. japonicum worms at different developmental stages and in male and female adult worms using real-time quantitative PCR (RT-qPCR) assay. Thirty female BALB/c mice at ages 6 to 8 weeks were divided into three groups, including the rSjHsc20 immunization group, the PBS control group, and the ISA 206 adjuvant group, of 10 mice in each group. Mice in the rSjHsc20 immunization group were subcutaneously immunized with 20 μg rSjHsc20 on days 1, 15 and 31, and animals in the PBS control group were subcutaneously injected with the same volume of PBS on days 1, 15 and 31, while mice in the ISA 206 adjuvant group were subcutaneously immunized with the same volume of ISA 206 adjuvant on days 1, 15 and 31, respectively. All mice in each group were infected with (40 ± 2) S. japonicum cercariae via the abdomen 14 day following the last immunization. Levels of serum specific IgG and its subtypes IgG1 and IgG2 antibodies against rSjHsc20, and the serum titers of anti-rSjHsc20 antibody were detected in mice using indirect enzyme-linked immunosorbent assay (ELISA). All mice were sacrifice 42 days post-infection, and S. japonicum worms were collected from the hepatic portal vein and counted. The eggs per gram (EPG), worm burden reductions and egg burden reductions were estimated to evaluate the protective efficacy of the rSjHsc20 protein. Results The SjHsc20 gene had an open reading frame (ORF) with 756 bp in length and encoded 252 amino acids, and the rSjHsc20 protein had a relative molecular mass of approximately 29 kDa. The rSjHsc20 protein was recognized by the serum of mice infected with S. japonicum and the serum of mice immunized with the rSjHsc20 protein, indicating that rSjHsc20 had a good immunogenicity. There was a significant difference in the transcriptional levels of the SjHsc20 gene among the 7-day (1.001 4 ± 0.065 7), 12-day (2.268 3 ± 0.129 2), 21-day (1.378 5 ± 0.160 4), 28-day (1.196 4 ± 0.244 0), 35-day (1.646 3 ± 0.226 1), 42-day worms of S. japonicum (1.758 0 ± 0.611 1) (F = 38.45, P < 0.000 1), and the transcriptional level of the SjHsc20 gene was higher in the 12-day worms than in worms at other developmental stages (all P values < 0.000 1). The serum levels of anti-rSjHsc20 IgG antibody were 0.106 6 ± 0.010 7, 0.108 3 ± 0.010 4, and 0.553 2 ± 0.069 1 in the PBS control group, ISA 206 adjuvant group, and rSjHsc20 immunization group following the last immunization, respectively, and the serum levels of IgG1 antibody were 0.137 3 ± 0.054 0, 0.181 1 ± 0.096 8, and 1.765 8 ± 0.221 1, while the levels of IgG2a antibody were 0.280 3 ± 0.197 6, 0.274 0 ± 0.146 3, and 1.560 4 ± 0.106 0, respectively. There were significant differences in the serum levels of anti-rSjHsc20 IgG (F = 397.70, P < 0.000 1), IgG1 (F = 401.00, P < 0.000 1) and IgG2a antibodies (F = 229.70, P < 0.000 1) among the three groups, and the serum levels of anti-rSjHsc20 IgG, IgG1 and IgG2a antibodies were higher in the rSjHsc20 immunization group than in the PBS control group and the ISA 206 adjuvant group (all P values < 0.000 1). There was a significant difference in the IgG1/IgG2a ratio among the rSjHsc20 immunization group (1.177 2 ± 0.143 6), the PBS control group (0.428 4 ± 0.199 8) and the ISA 206 adjuvant group (0.559 9 ± 0.181 1) (F = 43.97, P < 0.000 1), and the IgG1/IgG2a ratio was > 1 in the rSjHsc20 immunization group, which was higher than in the PBS control group and the ISA 206 adjuvant group (both P values < 0.000 1). The titers of serum anti-rSjHsc20 antibody were all above 1∶16 384 in the rSjHsc20 immunization group following immunizations on days 1, 15 and 31, indicating that the rSjHsc20 protein had a strong immunogenicity. The mean worm burdens were (16.60±5.75), (15.80±5.58) worms per mouse and (14.40±5.75) worms per mouse in the PBS control group, the ISA 206 adjuvant group and the rSjHsc20 immunization group 42 days post-infection with S. japonicum cercariae (F = 0.50, P > 0.05), and the EPG were 68 370 ± 22 690, 67 972 ± 19 502, and 41 075 ± 13 251 in the PBS control group, the ISA 206 adjuvant group and the rSjHsc20 immunization group (F = 4.55, P < 0.05), with lower EPG in the PBS control group and the ISA 206 adjuvant group than in the rSjHsc20 immunization group (both P values < 0.05). Immunization with the rSjHsc20 protein resulted in a worm burden reduction of 13.25% and an egg burden reduction of 39.92% relative to the PBS control group. Conclusions SjHsc20 is successfully cloned and expressed, and the rSjHsc20 protein induces partial immunoprotective effects in mice, which provides a basis for deciphering the biological functions of SjHsc20 and assessing the potential of SjH-sc20 as a vaccine candidate.

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.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β‑protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF‑κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 toco-regulate antioxidant activity. NF‑κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such asα-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.