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.

As a functional anatomical marker of cellular activity and neural circuitry， c-Fos protein has been extensively utilized in studies which investigate neuroendocrine regulation， autonomic nervous system activity， and behavioral responses to stress. Recent research in ophthalmology has revealed that dynamic c-Fos expression is closely associated with pathophysiological processes such as retinal ganglion cell apoptosis， visual cortical plasticity， photodamage repair， and angiogenesis. This review aims to summarize the mechanistic roles of c-Fos protein in ocular diseases including glaucoma， amblyopia， and retinopathy， as well as exploring potential therapeutic approaches targeting c-Fos modulation. The studies have shown that the activation of c-Fos protein can promote the development of neurons in the visual cortex， and the inhibition of c-Fos can delay the apoptosis of retinal ganglion cells， neuroglia and photoreceptors in retinal and optic nerve diseases.

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.

Driven by policy initiatives promoting the integration of digital-intelligent technologies with primary healthcare and the digital transformation of traditional Chinese medicine (TCM), Smart TCM has emerged as a pivotal strategy for enhancing primary healthcare services for major chronic diseases. This paper reviews the current status, challenges, and feasible pathways of Smart TCM in community-level management of cardiovascular–cerebrovascular diseases and tumors, which represent major chronic disease burdens. Our findings indicate that Smart TCM demonstrates emerging potential in primary healthcare for chronic diseases across the entire continuum of “prevention-screening-diagnosis-treatment-management.” However, several significant challenges persist, including data silos and security vulnerabilities, limited applicability of existing models to real-world clinical needs, and insufficient digital literacy among primary healthcare physicians and elderly patients. To address these constraints, this paper proposes a multidimensional strategy encompassing the development of secure and interoperable regional data platforms, lightweight intelligent devices and support services aligned with primary care capacity, unified technical and data standards with corresponding quality control systems, adaptive and dynamically updated artificial intelligent models, interdisciplinary workforce training and patient education programs, and enhanced policy and health insurance support. Overall, Smart TCM shows great promise for improving the efficiency of primary healthcare delivery and establishing innovative TCM-based chronic disease management paradigms.

Sichuan Province is a hilly schistosomiasis-endemic province in China with the highest endemicity and largest coverage. During the period of more than 70 years of integrated control, there are five distinct phases of schistosomiasis control in Sichuan Province, including baseline investigation, Oncomelania hupensis snail control, chemotherapy for both humans and livestock, management of source of Schistosoma japonicum infections, and sustained control and intensified surveillance and early warning, and a package of key interventions are implemented, including environmental modification, snail control with chemical treatment, large-scale chemotherapy for humans and livestock, management of source of S. japonicum infections, intensified integrated control, establishment of an early warning and surveillance system. As a result, schistosomiasis has been eliminated across all 63 disease-endemic counties (cities, districts) in Sichuan Province by June 2025. Government-led multi-sectoral collaborations, science and technology-supported precision control strategies, and a sustainable control model tailored to the ecological and socio-economic conditions in endemic regions are essential to achieving the target of schistosomiasis elimination in Sichuan Province.

ObjectiveTo investigate the antitumor effect and mechanism of Guiqi Yiyuan ointment on Lewis lung cancer mice based on the extracellular regulatory protein kinase （ERK） signaling pathway. MethodsA Lewis lung cancer mouse model was established. Except for the blank group， the model mice were randomly divided into the model group， Guiqi Yiyuan ointment low， medium， and high dose groups， and the extracellular ERK1/2 inhibitor group， with 10 mice per group. The Guiqi Yiyuan ointment was administered by gavage at doses of 1.75， 3.5， 7.0 g·kg^-1·d^-1 for the low， medium， and high dose groups， respectively. The ERK1/2 inhibitor group was given the ERK1/2 inhibitor LY3214996 （100 mg·kg^-1·d^-1） by gavage. The treatment was administered for 14 consecutive days， after which samples were collected. Tumor histopathological changes were observed using hematoxylin-eosin （HE） staining. Transmission electron microscopy was used to observe ultrastructural changes in tumor cells. Immunofluorescence was performed to measure the phosphorylation of ERK1/2 （p-ERK1/2） and the expression of programmed cell death ligand-1 （PD-L1） in tumor tissues. Western blot and real-time quantitative polymerase chain reaction （Real-time PCR） were used to detect the expression of p-ERK1/2， PD-L1， the autophagy marker Beclin-1， the autophagic protein p62， and the microtubule-associated protein light chains LC3Ⅰ and LC3Ⅱ at both the protein and gene levels. ResultsCompared with the model group， the average tumor weight was significantly reduced in the low and medium dose groups of Guiqi Yiyuan ointment （P<0.05）， and markedly reduced in the high dose and inhibitor groups （P<0.01）. Tumor cells in all treatment groups became progressively irregular， with ruptured nuclei and expanded areas of cell disintegration and necrosis. The number of organellar ablations in tumor tissues increased， and the number of autophagic vesicles also increased in all groups. The mean fluorescence intensity of p-ERK1/2 and PD-L1 was reduced in the low and medium dose groups of Guiqi Yiyuan ointment （P<0.05）， and significantly reduced in the high dose and inhibitor groups （P<0.01）. The mRNA expression of ERK1/2， PD-L1， Beclin-1， and p62 was reduced in the medium dose group （P<0.05）， while LC3Ⅰ/Ⅱ mRNA expression was elevated （P<0.05）. In the high dose and inhibitor groups， mRNA expression of ERK1/2， PD-L1， Beclin-1， and p62 was significantly reduced （P<0.01）， while LC3Ⅰ/Ⅱ mRNA expression was significantly increased （P<0.01）. Protein expression of p-ERK1/2， PD-L1， Beclin-1， and p62 was reduced in the medium dose group （P<0.05）， and LC3Ⅰ/Ⅱ protein expression was elevated （P<0.05）. In the high dose and inhibitor groups， protein expression of p-ERK1/2， PD-L1， Beclin-1， and p62 was significantly reduced （P<0.01）， while LC3Ⅰ/Ⅱ protein expression was significantly elevated （P<0.01）. ConclusionGuiqi Yiyuan ointment may inhibit the activation of the ERK signaling pathway， downregulate the expression of p-ERK1/2， promote autophagic flux in tumor cells， and regulate the expression of PD-L1， thereby exerting an inhibitory effect on tumor growth in Lewis lung cancer mice.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals

Peptide-drug conjugates (PDCs) have emerged as a promising modality in precision oncology, enabling targeted delivery of cytotoxic payloads while minimizing off-target toxicity. The integration of covalent warheads, such as those based on sulfur(VI) fluoride exchange (SuFEx) chemistry, enhances drug-target residence time and tumor accumulation. However, existing screening methods for covalent peptide (CP) libraries require post-translational warhead conjugation, limiting throughput. Here, we present an integrated mRNA display platform that incorporates covalent warheads during ribosomal synthesis, enabling efficient screening of ultra-diverse covalent macrocyclic peptide libraries (>10¹³ variants). This approach, using site-specific incorporation of N-chloroacetyl-d-phenylalanine and fluorosulfate-l-tyrosine, accelerated the discovery of irreversibly binding (K _i = 3.58 μmol/L) Nectin-4-targeting peptide CP-N1-N₃ via proximity-triggered SuFEx. The peptide was further conjugated to cytotoxic payloads, yielding the covalent PDC CP-N1-MMAE with potent cytotoxicity (IC₅₀ ≈ 43 nmol/L) against MDA-MB-468 cells. This platform establishes a new paradigm for precision covalent drug discovery.