The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

Objective: To explore the regulatory role of paternal involvement in parenting in the association between parental adverse childhood experiences (ACEs) and autism like behavioral problems in children aged 3-6, so as to provide a basis for intervention strategies targeting the intergenerational health effects of ACEs. Methods: A longitudinal study design was used to select 1 780 children aged 3-6 from 12 kindergartens in Wuhu City, Anhui Province in June 2021 as the subjects of the baseline survey by convenient sampling, and the follow up surveys were conducted once every six months, for a total of two follow ups. Pearson correlation analysis was used to examine the association of parental ACEs and paternal involvement in parenting with autism like behavioral problems in children. A binary Logistic regression model was used to explore the association between parental ACEs and autism like behavioral problems in children. Based on stratification, the potential role of paternal involvement in the association between parental ACEs and autism like behavioral problems in children aged 3-6 was explored. Results: Autism like behavioral problem score in children aged 3-6 was positively correlated with the total score ACEs of parents as well as scores of emotional abuse, physical abuse, sexual abuse, emotional neglect, physical neglect, community violence, peer bullying, and family dysfunction( r =0.18,0.16,0.18,0.14,0.06,0.05,0.12,0.18,0.09,all P <0.05). Autism like behavioral problem score in children was negatively correlated with score of paternal involvement in parenting( r =-0.16, P <0.01). After adjusting for covariates such as gender, age, and birth weight of children, binary Logistic regression analysis showed that children whose parents had experiences of childhood emotional abuse, physical abuse, sexual abuse, community violence, peer bullying, or family dysfunction had higher risks of developing autism like behavioral problems than those in the non exposed group ( OR =3.29, 3.28, 6.71, 2.34, 4.08, 2.02, all P <0.01). In the group with low paternal involvement in parenting, all dimensions of parental ACEs(emotional abuse, physical abuse, sexual abuse, community violence, peer bullying, and family dysfunction) were significantly associated with an increased risk of autism like behavioral problems in children ( OR =1.84-8.34, all P <0.05). In contrast, in the group with high paternal involvement in parenting, no statistically significant associations were found between any dimension of parental ACEs and children s autism like behavioral problems ( OR =0.57-1.82, all P >0.05). Conclusion A high level of paternal involvement in parenting can reduce the adverse effects of parental ACEs on the occurrence of autism like behaviors in preschool children.

Sepsis is a potentially fatal infectious disease that easily causes shock and numerous organ failures. The kidney is one of the most susceptible to injury. Early intervention and renal protection significantly minimize patient mortality. Oligomeric proanthocyanidin (OPC), a naturally occurring plant compound, has a high potential for renal protection. This study was aimed at exploring the potential renoprotective role of OPC in sepsis-related renal tubular injury. C57/B6 mice were intraperitoneally injected with lipopolysaccharide (LPS) to simulate sepsis-related acute kidney injury in vivo. Renal function and pathology were assessed. RNA sequencing examined OPC mechanisms against LPS-induced renal injury. Oxidative stress indicators and inflammatory cytokines in blood serum and renal tissues were evaluated. In vitro, MTT assays assess cell viability. Apoptosis cells were detected using Hoechst 33342 and propidium iodide staining. Western blot assessed PI3K/AKT and NFκB signaling pathway proteins. OPC reduced LPS-induced renal tubular injury, improved renal functions and pathological changes, restored glutathione content, superoxide dismutase activity, and catalase activity, inhibited malondialdehyde overproduction, and suppressed LPS-induced overproduction of pro-inflammatory cytokines and the decline of anti-inflammatory cytokines. OPC attenuated LPS-induced cell morphological injury, reduced cell viability loss, and recovered the changes in proteins involved in PI3K/AKT and NFκB signaling pathways in MTEC cells. OPC protects against LPSinduced renal tubular injury by counteracting oxidative stress, inhibiting inflammatory responses, activating the PI3K/AKT signaling pathway, and inhibiting the NFκB signaling pathway. It may provide a viable solution to lessen renal injury in patients with sepsis.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

ObjectiveTo analyze the "compatibility" relationship of sugars and glycosides and the heat-clearing and blood-cooling effect of the roots of four varieties of Rehmannia glutinosa and provide a basis for research on the pharmacodynamic material basis and quality control of R. glutinosa. MethodsThe content of sugars and glycosides in the roots of four varieties of R. glutinosa was determined during the growth period. The principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and the "compatibility" relationship of active components were employed to screen out the differential samples. A rat model of bleeding due to blood heat was used to verify the pharmacodynamic differences and the potential active components of differential samples. ResultsThe content and proportion characteristics of various components in roots of the four varieties of R. glutinosa during the expansion stage and the maturity stage had obvious differences. The proportion of phenylethanoid glycosides at the maturity stage was higher than that at the expansion stage. The R. glutinosa variety 85-5 had special quality characteristics among the tested varieties. All the samples alleviated the symptoms in the rat model. The effect of clearing heat and cooling blood was different between the maturity stage and the expansion stage， as well as between 85-5 samples at the maturity stage and other samples. The effect of clearing heat and cooling blood of R. glutinosa roots was the result of the combined action of multiple components in R. glutinosa roots and might be related to the high proportions of polysaccharides， iridoid glycosides， and phenylethanoid glycosides. ConclusionThe growth stage and variety affect the quality of R. glutinosa roots. The effect of clearing heat and cooling blood of R. glutinosa roots was related to the content and proportions of various components. The study can provide a basis for the basic research on the active components and quality control of R. glutinosa.

Sepsis is a potentially fatal infectious disease that easily causes shock and numerous organ failures. The kidney is one of the most susceptible to injury. Early intervention and renal protection significantly minimize patient mortality. Oligomeric proanthocyanidin (OPC), a naturally occurring plant compound, has a high potential for renal protection. This study was aimed at exploring the potential renoprotective role of OPC in sepsis-related renal tubular injury. C57/B6 mice were intraperitoneally injected with lipopolysaccharide (LPS) to simulate sepsis-related acute kidney injury in vivo. Renal function and pathology were assessed. RNA sequencing examined OPC mechanisms against LPS-induced renal injury. Oxidative stress indicators and inflammatory cytokines in blood serum and renal tissues were evaluated. In vitro, MTT assays assess cell viability. Apoptosis cells were detected using Hoechst 33342 and propidium iodide staining. Western blot assessed PI3K/AKT and NFκB signaling pathway proteins. OPC reduced LPS-induced renal tubular injury, improved renal functions and pathological changes, restored glutathione content, superoxide dismutase activity, and catalase activity, inhibited malondialdehyde overproduction, and suppressed LPS-induced overproduction of pro-inflammatory cytokines and the decline of anti-inflammatory cytokines. OPC attenuated LPS-induced cell morphological injury, reduced cell viability loss, and recovered the changes in proteins involved in PI3K/AKT and NFκB signaling pathways in MTEC cells. OPC protects against LPSinduced renal tubular injury by counteracting oxidative stress, inhibiting inflammatory responses, activating the PI3K/AKT signaling pathway, and inhibiting the NFκB signaling pathway. It may provide a viable solution to lessen renal injury in patients with sepsis.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Objective To construct machine learning models to predict the incidence of linezolid-induced thrombocytopenia(LIT).Methods A total of 198 patients treated with linezolid in a hospital between January 2020 and March 2024 were retrospectively included.Firstly,the patients were divided into LIT and non-LIT groups,and the basic characteristics of the two groups were compared.Then,the variables with significant differences between the two groups were selected as potential risk factors to construct models for predicting LIT,including Logistic regression,decision tree and random forest models,and the prediction performance of the models was evaluated and compared.Results There were 52(26.3％)patients developed LIT during the treatment.The univariate analysis showed significant differences in linezolid trough concentration(Cmin),baseline platelet counts and creatinine clearance,the incidence of cerebrovascular disease,acute respiratory distress syndrome,and abdominal infection in patients with and without LIT.Among the three models built based on these variables,the random forest model has the best predictive performance.The results of variable importance analysis based on random forest model showed that Cmin,baseline platelet count and combined with acute respiratory distress syndrome had higher importance scores.Conclusions The random forest model has high accuracy in predicting the occurrence of LIT,and the risk of LIT is higher in patients with higher levels of linezolid exposure and lower baseline platelets.