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.

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.

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.

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.

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.

Objective Sanguisorba officinalis is the principal medicinal ingredient of Sicaokeyin gel. Gallic acid is commonly used as a key indicator component for evaluating the quality of Sanguisorba officinalis and its preparations. To establish a HPLC method for the determinant of gallic acid in Sicaokeyin gel. Methods With gallic acid as reference substance，HPLC analysis was performed on a Agilent Eclipse XDB-C₁₈ column （4.6 mm ×25 mm，5 μm） with methanol-0.05%phosphoric acid （5∶95） under isocratic elution. The flow rate was 1.0 ml/min and the detection wavelength was 272 nm. The column temperature was 30℃, the injection volume was 10 μl. Results Gallic acid showed good linear relationship in the range of 1.004-50.18 μg/ml （r=0.999 9）. The average recovery was 102.9% （n=9）. Conclusion The method was easy to operate; the result was accurate and reliable, which could be used for the quality control of sanguisorba officinalis in Sicaokeyin gel.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of liquiritin,liquiritin apioside,verbascoside,narirutin,isoacteoside,apigetrin,hesperidin,isoliquiritin,ononin,liquiritigenin,glycyrrhizic acid,isoliquiritigenin,honokiol,obovatol,pogostone and magnolol in Jiawei Huoxiang Zhengqi Soft Capsules.METHODS The analysis was performed on a 40 ℃ thermostatic ZORBAX Eclipse Plus C18 column(2.1 mm×100 mm,1.8 μm),with the mobile phase comprising of 0.1％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning with multiple reaction monitoring mode.RESULTS Sixteen constituents showed good linear relationships within their own ranges(r＞0.990 0),whose average recoveries were 83.74％-105.12％with the RSDs of 1.10％-4.8％.CONCLUSION This accurate,sensitive,stable and reproducible method can provide a reference for the overall quality control of Jiawei Huoxiang Zhengqi Soft Capsules.

AIM To optimize the extraction process for total polyphenols from Conioselinum vaginatu(Spreng.)Thell.,make component analysis,and evaluate their anti-oxidant,hypoglycemic activities.METHODS The effects of ultrasound,enzymatic hydrolysis,acid hydrolysis,alcohol extraction and hydrolysis processes on the extraction quantity of total polyphenols were investigated,respectively.With extraction temperature,extraction time,ethanol concetration and liquid-solid ratio as influencing factors,extraction quantity of total polyphenols as an evaluation index,the extraction process was optimized by response surface method.HPLC was adopted in the identification of polyphenolic composition and determination of their contents.Subsequently,total polyphenols' scavenging capacities on DPPH,ABTS,OH free radicals,total reducing power and inhibitory capacity on α-glucosidase were determined.RESULTS The highest extraction quantity of total polyphenols was observable when extraction process was employed.The optimal conditions were determined to be 62 ℃ for extraction temperature,54 min for extraction time,69％for ethanol concentration,and 50∶1 for liquid-solid ratio,the extraction quantity of total polyphenols was(9.51±0.2)mg GAE/g.Seven constituents existed in C.vaginatu,among which ferulic acid demonstrated the highest content,followed by that of myricetin,while D-tryptophan content was the lowest.At the concentration of 7.61 mg/L,total polyphenols displayed the scavenging rates on DPPH,ABTS,OH free radicals of 80.70％,85.97％,28.60％,total reducing power of 0.22,and inhibition rate on α-glucosidase of 77.23％,respectively.CONCLUSION This stable and reliable method can be used for the extraction of total polyphenols from C.vaginatum with strong anti-oxidant,hypoglycemic activities.