Traditional Chinese medicine （TCM）， through its multi-target and systematic regulatory effects， has demonstrated unique advantages in the treatment of gastric precancerous lesions （GPL）. At present， TCM theoretical research on GPL is mainly reflected in three aspects， the integration of macroscopic syndrome differentiation， the inflammation-carcinoma transformation mechanism， as well as the systematization and scientization of theoretical inheritance from famous TCM practitioners. High-quality evidence-based research findings serve as the foundation for clinical practice guidelines on GPL， and TCM has gained international academic recognition in the field of GPL prevention and treatment. Research on TCM mechanisms has yielded a series of important outcomes in the aspects of signaling pathways， gene expression regulation， cellular epigenetics， histone modification， and intestinal microecology. It is proposed that future research on GPL should focus on four key directions， establishing multi-omics data， exploring targeted intervention strategies on key regulatory nodes， advancing the standardization process of integrated traditional Chinese and western medicine prevention and treatment technologies， and constructing stratified screening and intervention platforms. The in-depth integration of TCM microcosmic mechanism of action with its macroscopic syndrome differentiation and treatment system， coupled with interdisciplinary research， will provide valuable references for the clinical treatment and scientific research of GPL.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

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.

ObjectiveTo observe the clinical efficacy and possible mechanisms of Xianglian Huazhuo Granules （香连化浊颗粒， XHG） in the treatment of chronic atrophic gastritis with intestinal metaplasia. MethodsA total of 140 patients with chronic atrophic gastritis and intestinal metaplasia were randomly divided into a treatment group and a control group， with 70 cases in each group. The treatment group received 12.5 g of XHG orally， twice daily. The control group received 12.5 g of placebo orally， twice daily. Both groups were treated for 6 months. The traditional Chinese medicine （TCM） symptom scores， pathological types， serum tumor markers of the digestive system， and serum bile acids （TBA）， interleukin-23 （IL-23）， and Dickkopf-related protein 1 （DKK-1） levels were observed before and after treatment. Safety indicators and adverse events were recorded. After treatment， TCM syndrome efficacy and pathological types were evaluated， and patients were followed up for 18 months with gastric endoscopy and pathological results， which were compared with the results after treatment finished. ResultsTwo patients dropped out in the control group， and a total of 168 cases were included in the final analysis， 70 in the treatment group and 68 in the control group. The treatment group showed a significant reduction in TCM symptom scores， serum TBA， IL-23， and DKK-1 levels， and a significant increase in alpha-fetoprotein （AFP）， carbohydrate antigen 125 （CA125）， carbohydrate antigen 199 （CA199） levels； in the control group， carcinoembryonic antigen （CEA）， CA125， CA199 levels significantly increased （P＜0.05 or P＜0.01）； and carbohydrate antigen 242 （CA242） level in both the treatment group and the control group decreased significantly （P＜0.01）. The treatment group had lower TCM symptom scores and lower levels of serum TBA， IL-23， and DKK-1 compared to the control group （P＜0.05）. The effective rate for TCM syndrome efficacy in the treatment group was 80.00% （56/70）， significantly higher than the 20.59% （14/68） in the control group （P < 0.05）. The effective rate for pathological classification in the treatment group was 72.73% （8/11） for mixed intestinal metaplasia， significantly better than 46.15% （6/13） in the control group （P＜0.05）. No adverse events were reported in either group. Among 40 patients who had a follow-up endoscopy after one year， 21 were from the treatment group， of whom 11 showed reduced intestinal metaplasia， 9 showed no significant changes， and 1 had worsened； while 19 patients in the control group had 4 with reduced intestinal metaplasia， 13 with no significant changes， and 2 with worsened conditions. No cancer was detected in either group. The treatment group showed significantly better improvement in intestinal metaplasia on follow-up gastric endoscopy pathology than the control group （P＜0.05）. ConclusionXHG can significantly improve the clinical symptoms in patients with chronic atrophic gastritis and intestinal metaplasia and reduce the degree of mixed intestinal metaplasia. The mechanism may involve lowering serum TBA， DKK-1， and IL-23 levles， thus delaying the progression from inflammation to cancer.

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.

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

Objective Excavate the prescriptions containing the drug pair of Aurantii Fructus Immaturus-Magnoliae Officinalis Cortex(AFI-MOC),and statistically analyze the rules of prescription medication,and explore its potential mechanism of action in the treatment of food retention disorder.Methods The prescriptions containing the pair of AFI-MOC in the Great Dictionary of Traditional Chinese Medicine Prescriptions were retrieved and typed into Excel to establish a database.The source,dosage form,frequency of compatibility of traditional Chinese medicine,nature,taste and meridian attribution and indications were analyzed.Using R language(4.3.3)software with OriginPro to analysis co-occurrence frequency,association rule,correlation clustering analysis,and visualization.Then,the network construction,Protein-Protein Interaction Network(PPI)analysis,Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genome(KEGG)pathway enrichment analysis of the"AFI-MOC-active ingredient-food retention disorder target"network were performed for the AFI-MOC drug pair and its indications food retention disorder.The binding between the core active ingredients and key target proteins was evaluated by molecular docking.Results A total of 349 prescriptions containing the pair of AFI-MOC were included,involving 267 Chinese herbs.Among them,the high-frequency compatibility drugs included Citri Reticulatae Pericarpium,Glycyrrhizae Radix et Rhizoma,Atractylodis Macrocephalae Rhizoma,Rhei Radix et Rhizoma and Aucklandiae Radix.They are mainly warm in nature,spicy,bitter and sweet in taste,spleen,liver and stomach in meridian.There were 141 kinds of indications,most of which were diseases of the spleen and stomach system such as food retention disorder,dysentery and fullness.Correlation cluster analysis shows that the AFI-MOC drug pair is frequently combined with drugs that have the efficacy of promoting qi circulation,strengthening the spleen,clearing heat,and eliminating dampness.Found through the analysis of network pharmacology AFI-MOC drug pair on the core of the active ingredient of Luteolin,BU3,Naringenin,Honokiol and Nobiletin.Key targets for food retention disorder are BDNF,AKT1,ESR1,TNF and IL-6.Molecular docking results show Luteolin,combined with AKT1 is most closely.Conclusion AFI-MOC drug pair often compatible with Citri Reticulatae Pericarpium,Atractylodis Macrocephalae Rhizoma,Aucklandiae Radix.The advantages of the prescription containing AFI-MOC drug pair is food retention disorder.Its key active components can exert therapeutic effects through multiple targets such as AKT1 and TNF.This study reveals the AFI-MOC drug pair on compatibility laws,preliminary interpretation of the mechanism for the treatment of food retention disorder.It can provide references and a basis for studying the compatibility mechanism of AFI-MOC and guiding rational clinical medication.

Objective: To create a prediction model of multidrug resistant bacterial infections for inpatients with pneumonia, so as to provide the reference for the early identification and intervention of multidrug resistant bacterial infections. Methods: The inpatients with pneumonia in the Second Affiliated Hospital of Bengbu Medical University from October 2022 to June 2024 were selected as the research subjects. Basic information and clinical data of the patients were collected. Respiratory secretions were collected for etiological culture and drug sensitivity tests to analyze the infection situation of multidrug resistant bacteria. LASSO regression and a multivariable logistic regression model were used to screen predictive factors and establish a predictive model of multidrug resistant bacterial infections for inpatients with pneumonia. The predictive effect of the model was assessed by receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results: A total of 368 inpatients with pneumonia were recruited, including 215 males (58.42%) and 153 females (41.58%). The median age was 71.00 (interquartile range, 20.00) years. There were 168 cases of multidrug resistant bacterial infections detected, with a detection rate of 45.65%. The multivariable logistic regression analysis showed that long-term bedridden patients (OR=2.699, 95%CI: 1.120-6.504), use of antibiotics within 30 days (OR=8.623, 95%CI: 2.949-25.216), respiratory failure (OR=2.407, 95%CI: 1.058-5.478), intensive care unit treatment (OR=3.995, 95%CI: 1.313-12.161), and hypoproteinemia (OR=2.129, 95%CI: 1.012-4.480) were predict factors of multidrug resistant bacterial infections for inpatients with pneumonia. The area under the ROC curve of the established multidrug resistant bacterial infection prediction model was 0.909 (95%CI: 0.879-0.939). The calibration curve after repeated sampling calibration approached the standard curve, and the predicted values were highly consistent with the measured values. The decision curve showed that when the probability threshold is 0.27-0.99, the clinical net benefit for predicting the risk of multidrug resistant bacterial infection is relatively high. Conclusion The prediction model of multidrug resistant bacteria infection constructed has a good predictive value for multidrug resistant bacterial infection among inpatients with pneumonia

OBJECTIVE: To observe the clinical efficacy of Qihuang needle therapy for autism spectrum disorder (ASD) children with sleep disorder. METHODS: A total of 60 ASD children with sleep disorder were randomly divided into an observation group and a control group, 30 cases in each group. Both groups were treated with structured education intervention, 60 min each time, once a day, 6 times a week. Qihuang needle therapy was applied at Yintang (GV24⁺), Baihui (GV20) and bilateral Jueyinshu (BL14), Xinshu (BL15) in the observation group, multi-direction needling was delivered and without needle retaining. The treatment was given 2 times a week, each treatment was delivered at interval of 2 days at least. Behavioral intervention was adopted in the control group. Treatment for consecutive 12 weeks was required in both groups. Before and after treatment, the scores of children's sleep habits questionnaire (CSHQ), the autism behavior checklist (ABC), the childhood autism rating scale (CARS), and the childhood autism behavior scale (CABS) were observed in the two groups. RESULTS: After treatment, the scores of CSHQ, ABC, CARS and CABS were decreased compared with those before treatment (P<0.01), and the above scores in the observation group were lower than those in the control group (P<0.05). CONCLUSION Qihuang needle therapy can effectively treat ASD with sleep disorder, improve the core symptoms of ASD and the sleep quality.
Humans ; Autism Spectrum Disorder/physiopathology* ; Male ; Female ; Child ; Sleep Wake Disorders/physiopathology* ; Child, Preschool ; Acupuncture Therapy ; Acupuncture Points ; Treatment Outcome ; Sleep ; Needles