As a distinctive resource of Chinese civilization， traditional Chinese medicine （TCM） technology transfer faces significant opportunities under the background of digital and intelligent transformation， while also being constrained by unique challenges such as the complexity of its theoretical system， lengthy industrial chains， and multidimensional policy restrictions， resulting in a "high-value-high-threshold" paradox. At present， TCM technology transfer is deeply trapped in a "threefold reluctance" dilemma， i.e.， unwillingness to transfer， inability to transfer， and lack of capacity to transfer. Specifically， the disconnection between scientific research evaluation systems and market demand leads to low conversion rates of research achievements， unclear ownership and compliance risks suppress innovation incentives， and the absence of professional services intensifies supply-demand mismatches. This article systematically analyzes the specific characteristics of TCM technology transfer and proposes a breakthrough pathway centered on full-chain digital and intelligent transformation. By integrating technologies such as intelligent sorting systems， blockchain-based traceability， and AI diagnostic models， the TCM ecosystem spanning "cultivation-production-service" can be reconstructed. In terms of standardization， promoting the progression from "experience-based data conversion" to "data standardization" and further to "intelligent standardization" is advocated to resolve quality control challenges. For example， a "three-no-one-full" certification system can strengthen quality trust. Policy coordination should focus on optimizing mechanisms for the transformation of scientific and technological achievements， while exploring intellectual property securitization and risk-sharing models to stimulate research momentum. In terms of internationalization， reliance on the Belt and Road Initiative platform to promote the export of geo-authentic medicinal material brands and standards is recommended to build a dual-driven model of "technology plus culture". Looking ahead， through the construction of national-level databases， the cultivation of interdisciplinary talent， and the mutual recognition of international standards， a new paradigm of "scientific intelligent manufacturing" can be formed， providing systematic solutions for the modernization of TCM and global health governance.

This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

Objective: To compare the consistency between bioelectrical impedance analysis (BIA) and dual energy X ray absorptiometry (DXA) in measuring body composition among Tibetan children and adolescents and to explore the applicability of BIA in plateau region, so as to provide scientific and convenient body composition measurement support among children and adolescents. Methods: From May to June, 2022, a total of 344 Tibetan children and adolescents aged 6-17 years were selected from Golmud Municipal National Middle School and Changjiangyuan Nationality Primary School in Qinghai Province by cluster sampling method, and their fat mass, fat mass percentage and lean mass were measured by DXA and BIA. The consistency and correlation between the two methods were assessed by using the Wilcoxon rank-sum test, Spearman correlation analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: DXA measured fat mass and fat mass percentage were significantly higher than those obtained by BIA (6-12 years old: Z =9.91, 11.28; 13-17 years old: Z =9.02, 10.21), while lean mass and lean mass percentage were significantly lower than BIA results (6-12 years old: Z =-11.60, -11.30; 13-17 years old: Z =-10.77, -10.36) (all P < 0.05 ). The two methods showed strong correlations in fat mass and lean mass (all r >0.80, all ICC >0.90), but exhibited poor agreement in fat mass percentage and lean mass percentage (6-12 years old: Lin s CCC =0.64, 0.41; 13-17 years old: Lin s CCC = 0.79 , 0.35). Bland-Altman analysis showed that the difference between the two methods was negatively correlated with the average value in FM%(6-12 years old: r =-0.75, 13-17 years old: r =-0.79, both P <0.01). Conclusion BIA and DXA show high consistency in measuring body fat mass and lean body mass in Tibetan children and adolescents, although some bias is still present in certain individuals.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Objective:To explore the ultrasound changes,clinical efficacy and safety of ultrasound-guided compound betamethasone injection in the treatment of first metatarsophalangeal joint(MTP1)in acute gouty arthritis.Methods:This study was a prospective randomized controlled trial,71 patients diagnosed with MTP1 in acute gouty arthritis in Weifang People's Hospital from June 2022 to June 2024 were selected,they were divided into puncture treatment group of 36 cases and oral medication group of 35 cases according to the random number table method.The puncture treatment group was treated with ultrasound-guided compound betamethasone injection,and the oral medication group was treated with etocoxib combined with colchicine.Visual Analogue Scale(VAS),inflammatory indicators[erythrocyte sedimentation rate(ESR),C-reactive protein(CRP)],and semi-quantitative scores of musculoskeletal ultrasound of the two groups were compared,and the medication safety of the two groups was observed.Result:1,3 and 7 days after treatment,VAS score in the puncture treatment group decreased significantly compared with that in the oral medication group(P＜0.05).7 days after treatment,ESR and CRP in the puncture treatment group decreased more significantly,and semi-quantitative scores of musculoskeletal ultrasound showed that the improvement of joint effusion,synovial hyperplasia and blood flow signal was better than that in the oral medication group(P＜0.05).All 36 cases of puncture were successful at the first attempt without complications.Conclusion:Ultrasound-guided compound betamethasone injection in the treatment of MTP1 in acute gouty arthritis has a high success rate,it can rapidly reduce inflammation,relieve pain,restore joint function,and is safe to operate,it can be used as an effective treatment option.

Aim To investigate the protective effect of liraglutide(LIRA)on acetaminophen(APAP)-in-duced hepatotoxicity at the in vivo level and to reveal the underlying mechanism.Methods Forty SPF grade male C57BL/6J mice were randomly divided into the Control,LIRA(200 μg·kg-1),APAP(500 mg·kg-1),LIRA+APAP,LIRA+APAP+3-methylade-nine(3-MA,30 mg·kg-1)groups,with eight mice in each group.The mice were administered for three con-secutive days,and the materials were taken after 24 h.The general condition and body weight of mice in each group were recorded,and liver morphology was ob-served.Serum ALT and AST levels,as well as SOD ac-tivity,MDA,and GSH content in liver homogenates,were measured using biochemical assay kits.The levels of inflammatory cytokines IL-6,TNF-α,and IL-1β in serum were detected by ELISA.Liver pathological changes were assessed by HE staining,while mitochon-drial and autophagosome structures in liver tissues were observed using transmission electron microscopy.The number of PCNA-positive cells in liver tissues was e-valuated using immunohistochemical staining.The pro-tein expression levels of LC3Ⅱ,p62,Bax,Bcl-2,PC-NA,and CyclinD1 in liver tissues were determined by Western blot.Results LIRA pretreatment can im-prove the general condition of mice with acetamino-phen-induced liver injury(AILI),reduce serum ALT and AST levels,and effectively ameliorate the appear-ance and morphology of the liver as well as the patho-logical damage to liver tissue.Simultaneously,the lev-els of inflammatory cytokines IL-6,TNF-α,and IL-1βare significantly decreased;SOD activity and GSH con-tent are significantly increased,while MDA content is significantly reduced.Transmission electron microsco-py observations reveal the presence of numerous auto-phagosomes in the cytoplasm of liver tissue.Immuno-histochemical staining results indicate a significant in-crease in the number of PCNA-positive cells.Further-more,the expression of LC3Ⅱ,Bcl-2,PCNA,and Cy-clinD1 proteins in liver tissue is significantly upregulat-ed,while the expression of p62 and Bax proteins is significantly downregulated.However,after interven-tion with the autophagy inhibitor 3-MA,the aforemen-tioned protective effects of LIRA are significantly.Conclusions LIRA pretreatment can significantly im-prove liver injury in AILI mice.Its protective mecha-nism may be related to enhancing autophagy in hepato-cytes,thereby reducing oxidative stress,inflammatory response and apoptosis in liver of AILI mice.

Aim To establish a simple and practical method for visualizing neurovascular coupling in the mouse barrel cortex in vivo.Methods Male C57BL/6J mice received stereotaxic in-jections of pAAV-hSyn-jGCaMP7s-WPRE into the barrel cortex to monitor neuronal activity.Three weeks post-injection,a crani-al window was implanted,and TRITC-Dextran 155 ku was ad-ministered intravenously to visualize the vasculature and blood flow dynamics.A custom-built whisker stimulator was used to e-voke controlled neuronal excitation.Two-photon microscopy was employed to monitor neuronal and vascular responses to whisker stimulation in real-time.Results Neuronal calcium signals and plasma signals were clearly observed using two-photon microsco-py.Whisker stimulation led to a significant increase in neuronal calcium signals in the barrel cortex,indicating effective neuronal activation.This neuronal excitation was accompanied by a syn-chronous increase in blood vessel diameter,blood flow velocity and overall blood flow.Conclusions This study successfully establishes a three-dimensional visualization framework(spatial,temporal,and functional)for in vivo visualization of neurovascu-lar coupling in the mouse barrel cortex,which provides a useful tool for investigating the pathophysiological mechanisms of neuro-vascular dysfunction and evaluating the efficacy of potential ther-apies.

Aim To investigate the protective effects of methyl rosmarinate(MR)on myocardial injury in-duced by high-altitude hypoxia and explore its underly-ing mechanisms.Methods BALB/c mice were ran-domly divided into a control group,a model group,and low-,medium-,and high-dose MR groups(25,50,and 75 mg·kg-1,respectively).Except for the control group,all other groups were exposed to a hypobaric hypoxia chamber and administered MR via intraperitoneal injection daily for three days.After the experiment,myocardial tissues were collected for he-matoxylin and eosin(HE)staining to observe morpho-logical changes.Levels of malondialdehyde(MDA),glutathione(GSH),and superoxide dismutase(SOD)were measured to evaluate the anti-myocardial injury activity of MR.Network pharmacology was employed to predict drug-disease interaction targets,construct a protein-protein interaction(PPI)network,and identify core targets.Functional enrichment analysis was car-ried out using Gene Ontology(GO)and Kyoto Ency-clopedia of Genes and Genomes(KEGG)pathways.Molecular docking was used to verify the binding affini-ty of MR to core targets,and Western blot was conduc-ted to detect the expression of related proteins.Results MR significantly alleviated myocardial injury caused by high-altitude hypoxia.Network pharmacology analy-sis identified EGFR,Bcl-2,STAT3,MMP9,ESR1,and MTOR as key targets.Molecular docking con-firmed strong binding between MR and these core tar-gets.Western blot results demonstrated that MR im-proved myocardial injury by regulating the expression of STAT3,Bax,and Bcl-2 proteins.Conclusion MR may exert its protective effects on high-altitude hypoxi-a-induced myocardial injury through a multi-target mechanism.