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.

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.

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.

BACKGROUND: Quantitative flow ratio (QFR) holds significant value in guiding drug-coated balloon (DCB) treatment and enhancing outcomes. However, the predictive capability of post-angioplasty QFR for long-term clinical events in patients with de novo lesions who receive DCB treatment remains uncertain. The aim of this study was to explore the potential significance of post-angioplasty QFR measurements in predicting clinical outcomes in patients underwent DCB treatment for de novo lesions. METHODS: Patients who underwent DCB-only intervention for de novo lesions were enrolled. QFR was conducted after DCB treatment. The patients were then categorized based on post-angioplasty QFR. The primary endpoint was major adverse cardiac events (MACE), encompassing all-cause death, cardiovascular death, nonfatal myocardial infarction, stroke, and target vessel revascularization. RESULTS: A total of 553 patients with 561 lesions were included. The median follow-up period was 505 days, during which 66 (11.8%) MACEs occurred. Based on post-procedural QFR grouping, there were 259 cases in the high QFR group (QFR > 0.93) and 302 cases in the low QFR group (QFR ≤ 0.93). Kaplan-Meier analysis revealed a significantly higher cumulative incidence of MACE in the low QFR group (log-rank P = 0.004). The multivariate Cox proportional hazards model demonstrated a significant inverse correlation between QFR and the occurrence of MACEs (HR = 0.522, 95%CI: 0.289-0.942, P = 0.031). Landmark analysis indicated that high QFR had a significant reducing effect on the cumulative incidence of MACEs within 1 year (log-rank P = 0.016) and 1-5 years (log-rank P = 0.026). CONCLUSIONS In patients who underwent DCB-only treatment for de novo lesions, higher post-procedural QFR values (> 0.93) were identified as an independent protective factor against adverse prognosis.

Compared with total knee arthroplasty, unicondylar knee replacement has the advantage of preserving the knee tissue structure and motor function to the greatest extent. Pre-clinical in-vitro test is an important tool to evaluate the safety and effectiveness of unicondylar knee prostheses, and it is also a key focus of the product registration process. Through collection, comparison, and analysis of current regulations, technical standards, guidelines, and related research literature, this paper expounds on the relevant research methods for the pre-clinical in-vitrotesting of unicondylar knee prostheses. At the same time, in conjunction with current evaluation requirements and experience, the study discusses the focus of pre-clinical performance research for unicondylar knee prostheses during the registration process to clarify the performance evaluation requirements of this product category. This aims to provide a reference for the pre-clinical performance research of unicondylar knee prostheses and to standardize industry testing standards.
Knee Prosthesis ; Arthroplasty, Replacement, Knee ; Humans ; Prosthesis Design ; Materials Testing

OBJECTIVE: To elucidate the effect of Huanglian-Renshen-Decoction (HRD) on ameliorating type 2 diabetes mellitus by maintaining islet β -cell identity through regulating paracrine and endocrine glucagon-like peptide-1 (GLP-1)/GLP-1 receptor (GLP-1R) in both islet and intestine. METHODS: The db/db mice were divided into the model (distilled water), low-dose HRD (LHRD, 3 g/kg), high-dose HRD (HHRD, 6 g/kg), and liraglutide (400 µ g/kg) groups using a random number table, 8 mice in each group. The db/m mice were used as the control group (n=8, distilled water). The entire treatment of mice lasted for 6 weeks. Blood insulin, glucose, and GLP-1 levels were quantified using enzyme-linked immunosorbent assay kits. The proliferation and apoptosis factors of islet cells were determined by immunohistochemistry (IHC) and immunofluorescence (IF) staining. Then, GLP-1, GLP-1R, prohormone convertase 1/3 (PC1/3), PC2, v-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafA), and pancreatic and duodenal homeobox 1 (PDX1) were detected by Western blot, IHC, IF, and real-time quantitative polymerase chain reaction, respectively. RESULTS: HRD reduced the weight and blood glucose of the db/db mice, and improved insulin sensitivity at the same time (P<0.05 or P<0.01). HRD also promoted mice to secrete more insulin and less glucagon (P<0.05 or P<0.01). Moreover, it also increased the number of islet β cell and decreased islet α cell mass (P<0.01). After HRD treatment, the levels of GLP-1, GLP-1R, PC1/3, PC2, MafA, and PDX1 in the pancreas and intestine significantly increased (P<0.05 or P<0.01). CONCLUSION HRD can maintain the normal function and identity of islet β cell, and the underlying mechanism is related to promoting the paracrine and endocrine activation of GLP-1 in pancreas and intestine.
Animals ; Glucagon-Like Peptide 1/metabolism* ; Diabetes Mellitus, Type 2/metabolism* ; Glucagon-Like Peptide-1 Receptor/metabolism* ; Insulin-Secreting Cells/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Male ; Blood Glucose/metabolism* ; Insulin/blood* ; Mice ; Intestinal Mucosa/pathology* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Islets of Langerhans/pathology*

OBJECTIVE: To identify the underlying molecular mechanism of Modified Hu-Lu-Ba-Wan (MHW) in alleviating renal lesions in mice with diabetic kidney disease (DKD). METHODS: The db/db mice were divided into model group and MHW group according to a random number table, while db/m mice were settled as the control group (n=8 per group). The control and model groups were gavaged daily with distilled water [10 mL/(kg·d)], and the MHW group was treated with MHW [17.8 g/(kg·d)] for 6 weeks. After MHW administration for 6 weeks, indicators associated with glucolipid metabolism and urinary albumin were tested. Podocytes were observed by transmission electron microscopy. Kidney transcriptomics was performed after confirming therapeutic effects of MHW on DKD mice. The relevant target of MHW' effect in DKD was further determined by enzyme-linked immunosorbent assay, Western blot analysis, immunohistochemistry, and immunofluorescence staining. RESULTS: Compared with the model group, MHW improved glucose and lipid metabolism (P<0.05), and reduced lipid deposition in the kidney. Meanwhile, MHW reduced the excretion of urinary albumin (P<0.05) and ameliorated renal damage. Transcriptomic analysis revealed that the inflammation response, particularly the interleukin-17 (IL-17) signaling pathway, may be responsible for the effect of MHW on DKD. Furtherly, our results found that MHW inhibited IL-17A and alleviated early fibrosis in the diabetic kidney. CONCLUSION MHW ameliorated renal damage in DKD via inhibiting IL-17A, suggesting a potential strategy for DKD therapy.
Animals ; Diabetic Nephropathies/genetics* ; Interleukin-17/antagonists & inhibitors* ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Kidney/ultrastructure* ; Podocytes/metabolism* ; Mice ; Albuminuria ; Lipid Metabolism/drug effects* ; Mice, Inbred C57BL