OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

Chronic obstructive pulmonary disease (COPD) currently lacks effective treatments to halt disease progression, making the search for preventive and therapeutic drugs a pressing issue. Natural products, with their accessibility, affordability, and low toxicity, offer promising avenues. Investigating the pharmacological effects and related signaling mechanisms of active components from natural products on COPD animal models induced by various triggers has become an important focus. In animal models induced by cigarette smoke, cigarette smoke combined with lipopolysaccharide (LPS), air pollution, elastase, bacterial or viral infections, the active compounds of natural products, such as flavonoids, terpenoids, and phenolics, can exert anti-inflammatory, antioxidant, mucus-regulating, and airway remodeling-inhibiting effects through key signaling pathways including nuclear factor-erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and mitogen-activated protein kinase (MAPK). These findings not only provide a theoretical basis for the clinical diagnosis and treatment of COPD but also point to new directions for future scientific research.
Pulmonary Disease, Chronic Obstructive/etiology* ; Animals ; Disease Models, Animal ; Biological Products/pharmacology* ; Humans ; NF-kappa B/metabolism* ; Flavonoids/pharmacology* ; Signal Transduction/drug effects* ; Anti-Inflammatory Agents/pharmacology* ; Heme Oxygenase-1/metabolism* ; Terpenes/pharmacology* ; Antioxidants/pharmacology* ; NF-E2-Related Factor 2/metabolism* ; Smoke/adverse effects* ; Phenols/therapeutic use*

Epigallocatechin-3-gallate (EGCG), a bioactive polyphenol abundant in green tea, has garnered significant attention for its diverse therapeutic applications, ranging from antioxidant and anti-inflammatory effects to potential anticancer properties. Despite its immense promise, the practical utilization of EGCG in therapeutic settings as a medication has been hampered by inherent limitations of this drug, including poor bioavailability, instability, and rapid degradation. This review comprehensively explores the current challenges associated with the application of EGCG and evaluates the potential of nanoparticle-based formulations in addressing these limitations. Nanoparticles, with their unique physicochemical properties, offer a platform for the enhanced stability, bioavailability, and targeted delivery of EGCG. Various nanoparticle strategies, including polymeric nanoparticle, micelle, lipid-based nanocarrier, metal nanoparticle, and silica nanoparticle, are currently employed to enhance EGCG stability and pharmacological activity. This review concludes that the particle sizes of most of these formulated nanocarriers fall within 300 nm and their encapsulation efficiency ranges from 51% to 97%. Notably, the pharmacological activities of EGCG-loaded nanoparticles, such as antioxidative, anti-inflammatory, anticancer, and antimicrobial effects, are significantly enhanced compared to those of free EGCG. By critically analyzing the existing literature and highlighting recent advancements, this article provides valuable insights into the promising prospects of nanoparticle-mediated EGCG formulations, paving the way for the development of more effective and clinically viable therapeutic strategies.
Animals ; Humans ; Anti-Inflammatory Agents/administration & dosage* ; Antineoplastic Agents/administration & dosage* ; Antioxidants/administration & dosage* ; Biological Availability ; Catechin/analogs & derivatives* ; Micelles ; Particle Size ; Nanoparticle Drug Delivery System/chemistry*

Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
Biological Products/pharmacology* ; Artificial Intelligence ; Humans ; Drug Discovery/methods* ; Machine Learning ; Deep Learning

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Food-derived bioactive peptides (FBPs), particularly those with ten or fewer amino acid residues and a molecular weight below 1300 Da, have gained increasing attention for their safe, diverse structures and specific biological activities. The development of FBP-based functional foods and potential medications depends on understanding their structure‒activity relationships (SARs), stability, and bioavailability properties. In this review, we provide an in-depth overview of the roles of FBPs in treating various diseases, including Alzheimer's disease, hypertension, type 2 diabetes mellitus, liver diseases, and inflammatory bowel diseases, based on the literature from July 2017 to Mar. 2023. Subsequently, attention is directed toward elucidating the associations between the bioactivities and structural characteristics (e.g., molecular weight and the presence of specific amino acids within sequences and compositions) of FBPs. We also discuss in silico approaches for FBP screening and their limitations. Finally, we summarize recent advancements in formulation techniques to improve the bioavailability of FBPs in the food industry, thereby contributing to healthcare applications.
Humans ; Peptides/therapeutic use* ; Structure-Activity Relationship ; Functional Food ; Diabetes Mellitus, Type 2/drug therapy* ; Biological Availability ; Alzheimer Disease/drug therapy* ; Inflammatory Bowel Diseases/drug therapy* ; Hypertension/drug therapy* ; Liver Diseases/drug therapy* ; Bioactive Peptides, Dietary

OBJECTIVES: To explore the key role of myeloid-derived suppressive cells (MDSCs) in pre-metastatic niche (PMN) and analyze their interrelationships with the main components in the microenvironment using a mathematical model. METHODS: Mathematical descriptions were used to systematically analyze the functions of MDSCs in tumor metastasis and elucidate their association with the major components (vascular endothelial cells, mesenchymal stromal cells, and cancer-associated macrophages) contributing to the formation of the pre-metastatic microenvironment. Based on the formation principle of the pre-metastatic microenvironment of tumors, the key biological processes were assumed to construct a coupled partial differential diffusion equation model. The existence and uniqueness of the model solutions were investigated using approximation methods, the qualitative theory of partial differential equations and Banach's immovable point theorem, and numerical simulations were carried out by differential numerical methods to verify the reliability and accuracy of the model. RESULTS: The existence and uniqueness of the local and overall solutions of the model were proved using the approximation method, the qualitative theory of partial differential equations and Banach's immovable point theorem in combination with the regularity estimation of the local solutions and the embedding inequality. Numerical simulation results further validated the reliability of the model and demonstrated the important role of MDSCs in the pre-metastatic microenvironment of tumors, especially in angiogenesis and immunosuppression. CONCLUSIONS This study reveals the important functions of MDSCs in the pre-metastatic microenvironment of tumors through mathematical modeling and numerical simulation, which provides an important theoretical basis for understanding the mechanism of tumor metastasis and devising cancer treatment strategies.
Tumor Microenvironment ; Myeloid-Derived Suppressor Cells ; Neoplasm Metastasis ; Humans ; Models, Biological ; Models, Theoretical ; Neoplasms/pathology*

OBJECTIVES: To explore the molecular mechanism of Jiangzhi Huaban Decoction (JZHBD) for improving atherosclerosis through the "qi meridian-blood channels" pathway. METHODS: ApoE^-/- mouse models of atherosclerosis were established by high-fat diet feeding for 8 weeks, with C57BL/6 mice on a normal diet as the controls. Forty ApoE^-/- mouse models were randomized into model group, low-, medium-, and high-dose JZHBD treatment groups, and atorvastatin treatment group (n=8) for their respective treatments for 8 weeks. The changes in body weight and overall condition of the mice were monitored weekly. After the treatments, serum levels of TC, TG, HDL-C, LDL-C, TBA, ALT, and AST of the mice were measured, pathological changes in the liver and aortic root plaques were examined with HE staining, and lipid accumulation in the liver and aortic wall was assessed using Oil Red O staining. The core molecular mechanism was studied through transcriptomics, and the expressions of the key pathway proteins were confirmed using Western blotting and immunohistochemistry. RESULTS: Treatment with JZHBD significantly reduced blood lipid and total bile acid levels, improved liver function and hepatic steatosis, and decreased aortic lipid deposition and plaque area in the mouse models of atherosclerosis. Transcriptomic analysis suggested that the therapeutic mechanism of JZHBD involved reverse cholesterol transport, PPAR signaling, and the inflammatory pathways. In atherosclerotic mice, JZHBD treatment obviously up-regulated hepatic expressions of PPARγ, LXRα, ABCA1, ABCG1, and CYP7A1, down-regulated hepatic expressions of p-p65/p65, IL-6, IL1β in the liver, increased ABCG5 and ABCG8 expressions in the intestines, and decreased ICAM-1 and VCAM-1 expressions in the aortic plaques. CONCLUSIONS JZHBD improves atherosclerotic vascular damage and plaque formation possibly by regulating hepatic reverse cholesterol transport and inflammation via modulating the hepatic PPARγ/LXRα/NF-κB signaling pathway.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Inbred C57BL ; Plaque, Atherosclerotic/metabolism* ; Liver/metabolism* ; Mice ; Atherosclerosis/metabolism* ; Cholesterol/metabolism* ; PPAR gamma/metabolism* ; Male ; Diet, High-Fat ; Biological Transport

Human's robust cognitive abilities, including creativity and language, are made possible, at least in large part, by evolutionary changes made to the cerebral cortex. This paper reviews the biology and evolution of mammalian cortical radial glial cells (primary neural stem cells) and introduces the concept that a genetically step wise process, based on a core molecular pathway already in use, is the evolutionary process that has molded cortical neurogenesis. The core mechanism, which has been identified in our recent studies, is the extracellular signal-regulated kinase (ERK)-bone morphogenic protein 7 (BMP7)-GLI3 repressor form (GLI3R)-sonic hedgehog (SHH) positive feedback loop. Additionally, I propose that the molecular basis for cortical evolutionary dwarfism, exemplified by the lissencephalic mouse which originated from a larger gyrencephalic ancestor, is an increase in SHH signaling in radial glia, that antagonizes ERK-BMP7 signaling. Finally, I propose that: (1) SHH signaling is not a key regulator of primate cortical expansion and folding; (2) human cortical radial glial cells do not generate neocortical interneurons; (3) human-specific genes may not be essential for most cortical expansion. I hope this review assists colleagues in the field, guiding research to address gaps in our understanding of cortical development and evolution.
Humans ; Animals ; Biological Evolution ; Cerebral Cortex/metabolism* ; Neurogenesis/physiology* ; Signal Transduction/physiology* ; Hedgehog Proteins/metabolism* ; Ependymoglial Cells/physiology*

The rich club, as a community of highly interconnected nodes, serves as the topological center of the network. However, the similarities and differences in how the rich club supports functional integration and segregation in the brain across different species remain unknown. In this study, we first detected and validated the rich club in the structural networks of mouse, monkey, and human brains using neuronal tracing or diffusion magnetic resonance imaging data. Further, we assessed the role of rich clubs in functional integration, segregation, and balance using quantitative metrics. Our results indicate that the presence of a rich club facilitates whole-brain functional integration in all three species, with the functional networks of higher species exhibiting greater integration. These findings are expected to help to understand the relationship between brain structure and function from the perspective of brain evolution.
Animals ; Humans ; Brain/diagnostic imaging* ; Mice ; Male ; Nerve Net/diagnostic imaging* ; Macaca ; Female ; Neural Pathways/diagnostic imaging* ; Magnetic Resonance Imaging ; Biological Evolution ; Adult ; Diffusion Magnetic Resonance Imaging ; Brain Mapping ; Species Specificity ; Mice, Inbred C57BL