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.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

OBJECTIVE: To investigate the potential mechanisms of sepsis pathogenesis in intensive care unit (ICU), with a specific focus on the role of skin microbiota, and to evaluate the causal relationships between skin microbiota and ICU sepsis using Mendelian randomization (MR). METHODS: A two-sample MR analysis was performed using skin microbiota genome-wide association study (GWAS) summary data from German population cohorts as exposures, combined with ICU sepsis susceptibility and 28-day mortality GWAS summary data from the IEU OpenGWAS database as outcomes. The primary causal effect estimates were generated using the inverse variance weighted (IVW) method, supplemented by validation through MR-Egger and weighted median approaches. Heterogeneity and pleiotropy tests, along with sensitivity analyses, were conducted to evaluate the robustness of the results. RESULTS: Regarding risk of ICU sepsis, IVW analysis showed that order Pseudomonadales [odds ratio (OR) = 0.93, 95% confidence interval (95%CI) was 0.88-0.98], family Flavobacteriaceae (OR = 0.93, 95%CI was 0.90-0.96), and genus Acinetobacter (OR = 0.96, 95%CI was 0.93-0.99) were significantly negatively correlated with the risk of ICU sepsis (all P < 0.05). There was a significant positive correlation between the risk of ICU sepsis and the presence of β-Proteobacteria (OR = 1.05, 95%CI was 1.00-1.11) and Actinobacteria (OR = 1.05, 95%CI was 1.00-1.11), both P < 0.05. Regarding 28-day mortality of ICU sepsis, IVW analysis showed that phylum Bacteroidetes (OR = 0.92, 95%CI was 0.86-0.99), family Streptococcaceae (OR = 0.92, 95%CI was 0.85-0.98), family Flavobacteriaceae (OR = 0.90, 95%CI was 0.83-0.97), genus Streptococcus (OR = 0.92, 95%CI was 0.86-0.99), ASV016 [Enhydrobacter] (OR = 0.92, 95%CI was 0.87-0.98), and ASV042 [Acinetobacter] (OR = 0.92, 95%CI was 0.88-0.97) were significantly negatively correlated with the 28-day mortality of ICU sepsis (all P < 0.05); family Moraxellaceae (OR = 1.09, 95%CI was 1.00-1.18) and ASV008 [Staphylococcus] (OR = 1.08, 95%CI was 1.03-1.14) was significantly positively correlated with the 28-day mortality of ICU sepsis (both P < 0.05). Sensitivity analysis and MR-PRESSO showed no heterogeneity, pleiotropy, or horizontal pleiotropy between skin microbiota and ICU sepsis risk and 28-day mortality rate. Analysis of confounding factors showed that single nucleotide polymorphisms (SNPs) associated with relevant skin bacteria could independently and causally affect the risk of ICU sepsis or ICU sepsis related mortality rate, independent of other confounding factors. The Steiger test results indicated that the established causal relationship was not due to reverse causality. CONCLUSIONS Skin microbiota composition may influence both sepsis susceptibility and 28-day mortality in ICU settings. Family Flavobacteriaceae demonstrated protective effects against sepsis onset and mortality. These findings provide new perspectives for early detection and management strategies.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Mendelian Randomization Analysis ; Microbiota ; Skin/microbiology* ; Genome-Wide Association Study ; Risk Factors ; Skin Microbiome

Animal experiments are crucial in evaluating the preclinical safety and efficacy of new dental materials, drugs, instruments, and equipment by identifying and eliminating potential health risks to humans. An international team of several dental experts formulated a guideline named Preferred Reporting Items for Animal Studiesin Endodontology (PRIASE) 2021. Consisting of 11 domains, 43 individual items, and a flowchart. PRIASE provides guidance for animal experiments in dentistry and improves the quality of experiment design and reporting. This work introduces the process and basic content of the guideline and interprets the key items of its checklist with specific examples to provide reference for the reporting of animal experiment in dentistry in China.
Animals ; Animal Experimentation/standards* ; Checklist ; China ; Endodontics ; Guidelines as Topic ; Research Design

N⁶-methyladenosine (m⁶A) modification plays a critical role in cell cycle regulation, while the mechanism of m⁶A in regulating mitosis remains underexplored. Here, we found that the total m⁶A modification level in cells increased during mitosis by the liquid chromatography-mass spectrometry/mass spectrometry and m⁶A dot blot assays. Silencing methyltransferase-like 3 (METTL3) or METTL14 results in delayed mitosis, abnormal spindle assembly, and chromosome segregation defects by the immunofluorescence. By analyzing transcriptome-wide m⁶A targets in HeLa cells, we identified polo-like kinase 1 (PLK1) as a key gene modified by m⁶A in regulating mitosis. Specifically, through immunoblotting and RNA pulldown, m⁶A modification inhibits PLK1 translation via YTH N⁶-methyladenosine RNA binding protein 1, thus mediating cell cycle homeostasis. Demethylation of PLK1 mRNA leads to significant mitotic abnormalities. These findings highlight the critical role of m⁶A in regulating mitosis and the potential of m⁶A as a therapeutic target in proliferative diseases such as cancer.
Humans ; Polo-Like Kinase 1 ; Cell Cycle Proteins/metabolism* ; Proto-Oncogene Proteins/metabolism* ; Protein Serine-Threonine Kinases/metabolism* ; Mitosis/physiology* ; HeLa Cells ; Adenosine/genetics* ; Methyltransferases/metabolism* ; RNA, Messenger/metabolism* ; RNA-Binding Proteins/metabolism*

Objective To evaluate the efficacy and safety of systemic thrombolysis(ST)and standard anticoagulation(SA)in the treatment of lower extremity fracture complicated with distal deep vein thrombosis(DDVT).Methods We retrospectively analyzed the clinical data of 60 patients with lower extremity fracture complicated with DDVT treated from January 2021 to December 2023.When the lower limb venography indicated a calf thrombus burden score ≥3 points,a retrievable inferior vena cava filter(IVCF)was successfully placed in the healthy femoral vein before orthopedic surgery.The patients who received further anticoagulant or thrombolytic therapy after surgery were allocated into a ST group(n=30,urokinase ST and SA)and a SA group(n=30,only SA).The two groups were compared in terms of calf thrombus burden score,thrombus dissolution rate,IVCF placement time,IVCF retrieval rate,intercepted thrombi,hemoglobin level,platelet count,D-dimer level,and complications.Results There was no statistically significant difference in the calf thrombus burden score between the two groups before treatment(P=0.431).However,after treatment,the scores in both groups decreased(both P<0.001),with the ST group showing lower score than the SA group(P=0.002).The thrombus dissolution rate in the ST group was higher than that in the SA group(P<0.001).There was no statistically significant difference in the IVCF placement time between the two groups(P=0.359),and the IVCF retrieval rate was 100% in both groups.The ST group had fewer intercepted thrombi than the SA group(P=0.002).There was no statistically significant difference in hemoglobin level(P=0.238),platelet count(P=0.914),or D-dimer level(P=0.756)between the two groups before treatment.However,after treatment,both groups showed an increase in platelet count(both P<0.001)and a decrease in D-dimer level(both P<0.001).There was no statistically significant difference in the occurrence of complications between the two groups(P=0.704).Conclusions Both SA and ST demonstrate safety and efficacy in the treatment of lower extremity fractures complicated with DDVT,serving as valuable options for clinical application.Compared with SA,ST not only enhances the thrombus dissolution in the calf but also mitigates the risk of thrombosis associated with IVCF.
Humans ; Venous Thrombosis/therapy* ; Retrospective Studies ; Thrombolytic Therapy/methods* ; Male ; Female ; Middle Aged ; Fractures, Bone/complications* ; Lower Extremity/injuries* ; Anticoagulants/therapeutic use* ; Aged ; Treatment Outcome ; Adult

Objective:To investigate the characteristics and determinants of total scores of cerebral small vessel disease (CSVD) and to analyze the factors associated with enlarged perivascular space (EPVS) grading in pilots.Methods:The physical examination data of 72 pilots who were hospitalized and diagnosed with CSVD by MRI in the Air Force Medical Center (General Hospital of Air Force) between 2019 and 2022 was retrospectively analyzed. The pilots were grouped by the total CSVD score (0, 1, 2, 3, 4 points), and the distribution of CSVD imaging biomarkers was compared across groups. The severity of EPVS was classified into 3 levels: none or mild (0-10), moderate (11-20), and severe (>20). The impact of vascular risk factors on the total CSVD score and EPVS grading was analyzed.Results:The results of the total CSVD score showed that there were 19 cases (26.39%) with a score of 0, 43 cases (59.72%) with a score of 1, 10 cases (13.89%) with a score of 2, and 0 case with scores of 3 or 4. Among those who scored 1, there were 2 cases (4.65%) of lacunar infarction (LA), 1 case (2.33%) of moderate to severe white matter hyperintensity (WMH), 2 cases (4.65%) of cerebral microbleed (CMB), and 38 cases (88.37%) of moderate and severe EPVS. Among those who scored 2, there were 7 cases (70.00%) of LA combined with EPVS, 2 cases (20.00%) of CMB combined with EPVS, and 1 case (10.00%) of WMH combined with EPVS. According to the CSVD imaging classification of these pilots, there were 9 cases (12.50%) of LA, 52 cases (72.22%) of WMH, 4 cases (5.60%) of CMB and 61 cases (84.72%) of EPVS. Multiple ordered Logistic regression analysis showed that systolic blood pressure ( OR=1.068, 95% CI: 1.016-1.122) and high-density lipoprotein cholesterol ( OR=0.111, 95% CI: 0.015-0.843) made a difference in the total CSVD score. High-density lipoprotein cholesterol ( OR=0.166, 95% CI: 0.031-0.893) could affect the EPVS grading. Spearman′s correlation analysis showed that the systolic blood pressure level was positively correlated with the total CSVD score ( r=0.299, P=0.011), while the high-density lipoprotein cholesterol level was negatively correlated with the total CSVD score and EPVS grading ( r=-0.313, -0.263, P=0.041, 0.026). Conclusions:The total CSVD score of pilots is at a mild level with EPVS as the leading contributor. The systolic blood pressure and the high-density lipoprotein cholesterol level are determinants for the total CSVD score, while the high-density lipoprotein cholesterol level is a determinant for the EPVS grading of pilots. Blood pressure control and lipid regulation can go a long way towards preventing CSVD in pilots. The total CSVD score is of value for stratified evaluation and individual identification of pilots with CSVD.

Aim To investigate the anti-Zika virus(ZIKV)mechanism of diterpenoid compound 9 from Euphorbia helioscopia in vitro.Methods The cytotox-icity of compound 9 was evaluated using the CCK-8 as-say.A ZIKV-infected Vero cell model was established,and the antiviral activity was assessed through RT-qPCR,plaque assay,Western blot,and immunofluores-cence.Furthermore,the mechanism of action was elu-cidated using multi-cell line validation,nanoparticle tracking analysis,cellular thermal shift assay,and mo-lecular docking.Results In Vero cells,compound 9 exhibited an EC50 of(3.95±0.15)μmol·L-1 and a CC50 of(272.12±8.56)μmol·L-1,demonstrating significantly higher antiviral efficacy than the positive control drug ribavirin(RBV).Its virus inactivation effect was time-dependent and could significantly re-duce viral load and plaque formation.Studies revealed that compound 9 altered the physicochemical properties of ZIKV particles,including reducing surface charge and increasing particle size distribution.Additionally,it significantly enhanced the thermal stability of the prM protein.Molecular docking analysis indicated that compound 9 formed a high-affinity interaction with the prM protein(binding energy:-38.52 kJ·mol-1)and stabilized its structure through hydrophobic interac-tions.Conclusion Compound 9 exerts in vitro anti-ZIKV activity by directly inactivating the virus,disrup-ting viral particle integrity,and targeting the prM pro-tein.

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*