Objective: To explore the influencing factors of anxiety symptoms and to construct a predictive model based on machine learning algorithms, so as to provide support for the prevention and management of anxiety symptoms among junior high school students. Methods: From April to May 2023, a stratified random cluster sampling method was adopted to select 8 176 junior high school students from Zhengzhou and Shangqiu citys. All participants completed the Adolescent Self rating Life Events Checklist, the 10item Connor-Davidson Resilience Scale, the School Connectedness Scale, the Parent-Child Cohesion Questionnaire, and the 7 item Generalized Anxiety Disorder Scale. Logistic regression analysis identified the associated factors of anxiety symptoms among junior high school students. Predictive models were constructed using Logistic regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms, with SHapley Additive exPlanations analysis explaining the optimal model. Results: The detection rate of anxiety symptoms among junior high school students was 16.3%. Logistic regression analysis showed that junior high school students who were female ( OR =1.22), in the ninth grade ( OR =1.27), living in urban areas ( OR =1.37), having a father with a college education or above ( OR =1.26), having a mother with a senior high school education ( OR =1.26), and experiencing higher levels of negative life events ( OR =1.05) reported a higher risk of anxiety symptoms(all P <0.05). In contrast, those with moderate family economic status ( OR =0.71), moderate academic burden ( OR =0.59), low academic burden ( OR =0.54), moderate sleep quality ( OR =0.46), good sleep quality ( OR =0.26), excellent sleep quality ( OR =0.15), higher levels of psychological resilience ( OR =0.96), higher levels of school connectedness ( OR =0.96), and higher levels of parent-child cohesion ( OR =0.98) reported a lower risk of anxiety symptoms (all P <0.05). Three machine learning models demonstrated good predictive performance for anxiety symptoms among junior high school students (all AUC>0.8), with the XGBoost model achieving the best predictive performance. SHAP analysis revealed that negative life events, sleep quality, school connectedness, psychological resilience and parent-child cohesion were the top five relevant factors for predicting anxiety symptoms. Conclusions The detection rate of anxiety symptoms among junior high school students is relatively high. The XGBoost model is the optimal predictive model for anxiety symptoms in the population. Negative life events, sleep quality, school connectedness, psychological resilience, and parent-child cohesion are significant correlates of anxiety symptoms among junior high school students.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

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.

ObjectiveTranscranial magneto-acoustic stimulation (TMAS) is an emerging non-invasive neuromodulation technique that may provide a novel non-pharmacological intervention strategy for Parkinson's disease (PD). PD is characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc), leading to motor impairments such as bradykinesia, tremor, and rigidity. Increasing evidence indicates that mitochondrial dysfunction and impaired mitochondrial quality control are central mechanisms underlying dopaminergic neuronal loss. In particular, abnormalities in mitophagy and mitochondrial fission-fusion balance contribute substantially to oxidative stress, energy metabolic failure, and neuronal injury. At present, most clinical treatments for PD mainly alleviate symptoms but do not effectively halt disease progression. Therefore, exploring new interventions targeting the core pathological mechanisms is of considerable significance. This study aims to investigate whether TMAS can improve neural damage and motor dysfunction in PD mice by regulating mitophagy and the fission/fusion dynamic balance, thereby providing theoretical and experimental support for its application in PD treatment. MethodsMale C57BL/6 mice were used in this study. A PD model was established by intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) for 7 consecutive days. After model induction, mice in the intervention group received TMAS once daily for 14 consecutive days, whereas the corresponding control group received sham stimulation. The stimulation target was positioned over the primary motor cortex (M1). Motor performance was evaluated using the pole test and the open-field test. To verify the activation effect of TMAS on the target cortical region, c-Fos immunohistochemistry was performed in the M1. To assess nigral dopaminergic neuronal injury, tyrosine hydroxylase (TH) immunohistochemistry was used to quantify TH-positive neurons in the SNc. Mitochondrial function was evaluated by measuring reactive oxygen species (ROS) levels and adenosine triphosphate (ATP) content in the SNc. Western blot was further performed to determine the expression of mitophagy-related proteins, including PINK1, Parkin, LC3-II, and p62, as well as mitochondrial dynamics-related proteins, including Drp1 and Opa1. ResultsTMAS significantly increased the number of c-Fos-positive cells in M1 (P<0.000 1), indicating effective activation of neurons in the targeted cortical region. Compared with the control group, MPTP-treated mice exhibited marked motor dysfunction, including a significant reduction in total distance traveled in the open-field test (P<0.000 1) and mean speed (P=0.000 1), as well as significant prolongation of turn time and total climbing time in the pole test (P<0.000 1). These behavioral impairments were accompanied by a substantial loss of TH-positive dopaminergic neurons in the SNc, whereas TMAS significantly increased TH-positive neuron survival (P<0.000 1). In parallel, MPTP induced a pronounced increase in ROS levels and a significant reduction in ATP content, indicating severe mitochondrial dysfunction and energy metabolism impairment (P<0.01). TMAS treatment significantly improved motor performance, as reflected by the reversal of MPTP-induced impairment in the open-field and pole tests, and significantly reduced ROS accumulation (P<0.01) while restoring ATP production (P<0.001). At the molecular level, MPTP markedly downregulated PINK1 and Parkin, decreased p62 expression, increased LC3-II accumulation, elevated Drp1 expression, and reduced Opa1 expression, whereas TMAS significantly reversed these abnormalities, suggesting restoration of mitophagy-related mitochondrial quality control and re-establishment of mitochondrial fission-fusion balance. Collectively, these findings indicate that TMAS ameliorates MPTP-induced neurotoxicity and restores mitochondrial homeostasis and energy metabolism. ConclusionTMAS effectively attenuates neural damage and improves motor dysfunction in MPTP-induced PD mice. Its neuroprotective effects are closely associated with multidimensional regulation of the mitochondrial quality control system, including restoration of PINK1/Parkin-mediated mitophagy and rebalancing of Drp1/Opa1-related mitochondrial dynamics. Rather than acting only as a symptomatic neuromodulatory intervention, TMAS may influence a key pathological axis of PD by improving mitochondrial homeostasis in SNc and protecting nigral dopaminergic neurons. These findings provide experimental evidence supporting TMAS as a promising non-invasive physical intervention for PD.

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.

ObjectiveTranscranial magneto-acoustic stimulation (TMAS) is an emerging non-invasive neuromodulation technique that may provide a novel non-pharmacological intervention strategy for Parkinson's disease (PD). PD is characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc), leading to motor impairments such as bradykinesia, tremor, and rigidity. Increasing evidence indicates that mitochondrial dysfunction and impaired mitochondrial quality control are central mechanisms underlying dopaminergic neuronal loss. In particular, abnormalities in mitophagy and mitochondrial fission-fusion balance contribute substantially to oxidative stress, energy metabolic failure, and neuronal injury. At present, most clinical treatments for PD mainly alleviate symptoms but do not effectively halt disease progression. Therefore, exploring new interventions targeting the core pathological mechanisms is of considerable significance. This study aims to investigate whether TMAS can improve neural damage and motor dysfunction in PD mice by regulating mitophagy and the fission/fusion dynamic balance, thereby providing theoretical and experimental support for its application in PD treatment. MethodsMale C57BL/6 mice were used in this study. A PD model was established by intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) for 7 consecutive days. After model induction, mice in the intervention group received TMAS once daily for 14 consecutive days, whereas the corresponding control group received sham stimulation. The stimulation target was positioned over the primary motor cortex (M1). Motor performance was evaluated using the pole test and the open-field test. To verify the activation effect of TMAS on the target cortical region, c-Fos immunohistochemistry was performed in the M1. To assess nigral dopaminergic neuronal injury, tyrosine hydroxylase (TH) immunohistochemistry was used to quantify TH-positive neurons in the SNc. Mitochondrial function was evaluated by measuring reactive oxygen species (ROS) levels and adenosine triphosphate (ATP) content in the SNc. Western blot was further performed to determine the expression of mitophagy-related proteins, including PINK1, Parkin, LC3-II, and p62, as well as mitochondrial dynamics-related proteins, including Drp1 and Opa1. ResultsTMAS significantly increased the number of c-Fos-positive cells in M1 (P<0.000 1), indicating effective activation of neurons in the targeted cortical region. Compared with the control group, MPTP-treated mice exhibited marked motor dysfunction, including a significant reduction in total distance traveled in the open-field test (P<0.000 1) and mean speed (P=0.000 1), as well as significant prolongation of turn time and total climbing time in the pole test (P<0.000 1). These behavioral impairments were accompanied by a substantial loss of TH-positive dopaminergic neurons in the SNc, whereas TMAS significantly increased TH-positive neuron survival (P<0.000 1). In parallel, MPTP induced a pronounced increase in ROS levels and a significant reduction in ATP content, indicating severe mitochondrial dysfunction and energy metabolism impairment (P<0.01). TMAS treatment significantly improved motor performance, as reflected by the reversal of MPTP-induced impairment in the open-field and pole tests, and significantly reduced ROS accumulation (P<0.01) while restoring ATP production (P<0.001). At the molecular level, MPTP markedly downregulated PINK1 and Parkin, decreased p62 expression, increased LC3-II accumulation, elevated Drp1 expression, and reduced Opa1 expression, whereas TMAS significantly reversed these abnormalities, suggesting restoration of mitophagy-related mitochondrial quality control and re-establishment of mitochondrial fission-fusion balance. Collectively, these findings indicate that TMAS ameliorates MPTP-induced neurotoxicity and restores mitochondrial homeostasis and energy metabolism. ConclusionTMAS effectively attenuates neural damage and improves motor dysfunction in MPTP-induced PD mice. Its neuroprotective effects are closely associated with multidimensional regulation of the mitochondrial quality control system, including restoration of PINK1/Parkin-mediated mitophagy and rebalancing of Drp1/Opa1-related mitochondrial dynamics. Rather than acting only as a symptomatic neuromodulatory intervention, TMAS may influence a key pathological axis of PD by improving mitochondrial homeostasis in SNc and protecting nigral dopaminergic neurons. These findings provide experimental evidence supporting TMAS as a promising non-invasive physical intervention for PD.

ObjectiveTo investigate the role and mechanism of ribosomal DNA （rDNA） transcription and ribosome biogenesis in muscle atrophy induced by acute kidney injury （AKI）. MethodsEight male C57BL/6 mice were randomly divided into a control group （Ctrl） and a model group （AKI）. An AKI model was established via intraperitoneal injection of cisplatin. Muscle atrophy was phenotypically assessed by measuring muscle mass， myofiber cross-sectional area （HE staining）， and mRNA expression levels of atrophy-related genes （Murf-1， Atrogin-1， Igf-1） using qRT-PCR. In vivo protein synthesis rates were determined via the SUnSET assay （puromycin incorporation）. Ribosome biogenesis was evaluated by assessing rRNA content and 47S pre-rRNA expression levels. Myotubes differentiated from mouse skeletal muscle cell lines （C2C12 myotubes） were treated with serum from AKI mice， and the effects on rDNA transcription， ribosome biogenesis， and protein metabolism were analyzed using chromatin immunoprecipitation followed by quantitative polymerase chain reaction （ChIP-qPCR） and Western blot. ResultsAKI successfully induced muscle atrophy， as evidenced by a significant reduction in skeletal muscle mass. The most pronounced decrease occurred in the extensor digitorum longus muscle （21.0%， P 0.01）， along with a trend toward reduced myofiber cross-sectional area. At the molecular level， AKI inhibited muscle protein synthesis （83.14% reduction in puromycin incorporation， P 0.000 1） and impaired ribosome biogenesis， manifested by suppressed rDNA transcription elongation （52.62% decrease in 47S pre-rRNA ITS-1 levels， P 0.01） and reduced total rRNA content （65.29%， P 0.000 1）. In contrast， serum from AKI mice promoted rDNA transcription initiation and protein synthesis in C2C12 myotubes in vitro. ConclusionAKI induces muscle atrophy by suppressing rDNA transcription and ribosome biogenesis in skeletal muscle， leading to impaired protein synthesis. Dysregulated ribosome biogenesis may play a critical role in AKI-induced muscle atrophy.

Myocardial infarction （MI） and osteoporosis （OP）， as two prevalent metabolic diseases with high morbidity and mortality rates， are respectively characterized by cardiovascular system dysfunction and bone homeostasis imbalance， collectively posing significant global public health challenges. While clinically often considered as independent diseases， recent studies have revealed shared pathological mechanisms between the two. This study initiated its exploration from the traditional Chinese medicine concept of the "heart-bone" axis， systematically analyzing the correlation between MI and OP from perspectives including hemodynamics， neuroendocrinology， calcium homeostasis， inflammation and vascular injury， as well as hormone levels. By discussing the pathological mechanisms of "heart disease affecting the bones and bone disease affecting the heart"， the study also elucidated advancements in both Western and traditional Chinese medicine treatments. The goal is to provide novel insights and methodologies for the prevention and treatment of "heart-bone comorbidities"， thereby facilitating comprehensive management of cardiovascular and skeletal diseases.

Myocardial infarction （MI） and osteoporosis （OP）， as two prevalent metabolic diseases with high morbidity and mortality rates， are respectively characterized by cardiovascular system dysfunction and bone homeostasis imbalance， collectively posing significant global public health challenges. While clinically often considered as independent diseases， recent studies have revealed shared pathological mechanisms between the two. This study initiated its exploration from the traditional Chinese medicine concept of the "heart-bone" axis， systematically analyzing the correlation between MI and OP from perspectives including hemodynamics， neuroendocrinology， calcium homeostasis， inflammation and vascular injury， as well as hormone levels. By discussing the pathological mechanisms of "heart disease affecting the bones and bone disease affecting the heart"， the study also elucidated advancements in both Western and traditional Chinese medicine treatments. The goal is to provide novel insights and methodologies for the prevention and treatment of "heart-bone comorbidities"， thereby facilitating comprehensive management of cardiovascular and skeletal diseases.

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.