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.

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

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.

OBJECTIVES: To investigate the effect of avitinib for suppressing NLRP3 inflammasome activation and alleviating septic shock and explore the underlying mechanism. METHODS: Mouse bone marrow-derived macrophages (BMDM), human monocytic leukemia cell line THP-1, and peripheral blood mononuclear cells (PBMC) isolated from healthy volunteers were pre-treated with avitinib, followed by activation of the canonical NLRP3 inflammasome using agonists including nigericin, monosodium urate (MSU) crystals, or adenosine triphosphate (ATP). Non-canonical NLRP3 inflammasome activation was induced via intracellular transfection of lipopolysaccharide (LPS). Western blotting was used to detect the secretory protein markers of NLRP3 inflammasome activation and assess pyroptosis, and the levels of inflammatory cytokines in cell culture supernatant were determined with ELISA. In a mouse model of LPS-induced septic shock, the effect of avitinib treatment on the levels of inflammatory cytokines in serum and peritoneal lavage fluid were examined with ELISA, and survival curves of the mice were plotted using the Kaplan-Meier method. RESULTS: Avitinib significantly inhibited NLRP3 inflammasome activation in multiple cell types, and dose-dependently reduced IL-1β secretion and caspase-1 cleavage while suppressing GSDMD-mediated pyroptosis without obviously affecting IL-6 or TNF-α levels. In the mouse models of LPS-induced septic shock, avitinib significantly lowered IL-1β levels in serum and peritoneal fluid and extended survival time of the mice. CONCLUSIONS Avitinib suppresses NLRP3 inflammasome activation and alleviates septic shock in mice.
Animals ; Shock, Septic/metabolism* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein ; Inflammasomes/drug effects* ; Humans ; Macrophages/metabolism* ; Interleukin-1beta/metabolism* ; Lipopolysaccharides

Microglial pyroptosis and neuroinflammation have been implicated in the pathogenesis of sepsis-associated encephalopathy (SAE). OGT-mediated O-GlcNAcylation is involved in neurodevelopment and injury. However, its regulatory function in microglial pyroptosis and involvement in SAE remains unclear. In this study, we demonstrated that OGT deficiency augmented microglial pyroptosis and exacerbated secondary neuronal injury. Furthermore, OGT inhibition impaired cognitive function in healthy mice and accelerated the progression in SAE mice. Mechanistically, OGT-mediated O-GlcNAcylation of ATF2 at Ser44 inhibited its phosphorylation and nuclear translocation, thereby amplifying NLRP3 inflammasome activation and promoting inflammatory cytokine production in microglia in response to LPS/Nigericin stimulation. In conclusion, this study uncovers the critical role of OGT-mediated O-GlcNAcylation in modulating microglial activity through the regulation of ATF2 and thus protects against SAE progression.
Animals ; Microglia/metabolism* ; Pyroptosis/physiology* ; Mice ; Sepsis-Associated Encephalopathy/prevention & control* ; Activating Transcription Factor 2/metabolism* ; N-Acetylglucosaminyltransferases/genetics* ; Mice, Inbred C57BL ; Male ; Mice, Knockout

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design