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 evaluate the clinical efficacy and safety of Xiaozhen Formula （消疹方） in preventing skin toxicity induced by epidermal growth factor receptor inhibitors （EGFRIs） in patients with EGFR-mutant non-small cell lung cancer （NSCLC）. MethodsA randomized， double-blind， placebo-controlled， multicenter clinical study was conducted. A total of 120 patients with EGFR-mutant NSCLC from seven centers were enrolled and randomly assigned to a treatment group （60 cases） or a control group （60 cases）. On the basis of EGFRI-targeted therapy， patients in the treatment group received Xiaozhen Formula granules， whereas those in the control group received Xiaozhen Formula placebo granules， both at 10 g twice daily for 4 consecutive weeks. The primary outcomes were the grading of skin toxicity evaluated using the National Cancer Institute Common Terminology Criteria for Adverse Events （NCI-CTCAE） version 5.0 and the incidence of rash. Secondary outcomes included the time to onset and time to resolution of the highest-grade rash， the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 （EORTC QLQ-C30） scores， the Hospital Anxiety and Depression Scale （HADS） scores， and disease control rate， together with safety assessments. ResultsBased on the full analysis set （FAS）， the incidence of skin toxicity in the treatment group was 18.33% （11/60）， significantly lower than 45.00% （27/60） in the control group （P＜0.01）， with the highest severity of skin toxicity in both groups was grade 2. Among patients who developed rash， the resolution rate in the treatment group was 90.91% （10/11）， higher than that 33.33% （9/27） in the control group. The median time to resolution was 15 days （95%CI： 8-17 days） in the treatment group and it was not reached in the control group； the distribution of time to resolution differed significantly between groups （P＜0.001）. After treatment， the treatment group had higher EORTC QLQ-C30 functional scale and global health status/quality-of-life scores， and lower symptom scale， single-item scores， as well as HADS-A， and HADS-D scores than the control group （P＜0.05）. In the FAS analysis， the disease control rate （DCR） was 85.0% （51/60） in the treatment group， lower than 100.0% （60/60） in the control group （P=0.003）， whereas no significant between-group difference was observed in the per-protocol set （PPS） analysis （P=0.464）. The incidence of adverse events did not differ significantly between the two groups （P＞0.05）， and no definite safety signals related to the investigational drug were observed. ConclusionXiaozhen Formula can reduce the incidence of EGFRI-induced skin toxicity， and improve the outcome of rash regression， the patients' quality of life and psychological status in EGFR-mutant NSCLC patients.

ObjectiveTo investigate the application of central auditory testing combined with electroencephalographic features in the early screening of mild cognitive impairment due to Alzheimer's disease. MethodsA convenience sampling method was used to conduct a field survey in the Qixia District of Nanjing City, Jiangsu Province, from July to September, 2025. A total of 40 older adults diagnosed as mild cognitive impairment due to Alzheimer's disease and 23 cognitively normal older adults were recruited. Auditory N-back and dichotic listening paradigms were employed for assessment, while the scores of Montreal Cognitive Assessment Beijing Version (MoCA-BJ) and 32-channel electroencephalogram (EEG) data were simultaneously collected. Binary logistic regression analysis was applied to identify predictive indicators, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. ResultsThe total score of dichotic listening was positively correlated with overall cognitive function (r = 0.475, P < 0.01), while right-ear advantage showed negative correlations with overall cognition, visuospatial ability, abstract reasoning, and delayed recall (r < -0.276, P < 0.05). Task performance across all conditions of the N-back task was positively correlated with overall cognitive function (r > 0.249, P < 0.05). Regarding EEG features, δ and θ band power and power peaks were positively correlated with delayed recall (r > 0.258, P < 0.05), whereas (α+θ)/(α+β) and θ/(α+β) were positively correlated with naming ability and visuospatial executive function, respectively (r > 0.261, P < 0.05). The results of the binary logistic regression analysis showed that the total score of binaural dichotic listening, the accuracy of 1-back task, the average power of δ waves, and the ratio of θ/(α+β) were independent influencing factors for mild cognitive impairment due to Alzheimer's disease (P < 0.05). The ROC analysis indicated that the AUC for auditory task performance and EEG features were 0.845 and 0.735 respectively, and when combined, the AUC increased to 0.862. ConclusionThe screening model based on the integration of auditory task performance and EEG features can be used in the early identification of mild cognitive impairment due to Alzheimer's disease.

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

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 To investigate the ameliorative effect of deferoxamine(DFO)on cognitive impairment in a rat model of type 1 diabetes mellitus(T1DM)and to elucidate the molecular mechanisms underlying cerebral iron overload in T1DM rats.Methods Thirty-six healthy male SD rats(weighing 180～250 g)were randomly assigned into a blank control group(Ctrl),a T1DM model group and a DFO group,with 12 rats in each group.A single dose of 65 mg/kg streptozotocin(STZ)was intraperitoneally injected to the rats to establish a T1DM model,and those with fasting blood glucose≥16.7 mmol/L at 3 d later were designated as the T1DM group.Intracerebroventricular administration of DFO(5 μg/kg·d)was given to the DFO group for 28 consecutive days since 21 d after STZ injection.Morris water maze test was carried out to assess the spatial learning and memory abilities.Nissl staining and immunofluorescence assay were applied to observe neuronal morphology and number in the hippocampus and cortex.The iron content in the hippocampus was measured with inductively coupled plasma mass spectrometry(ICP-MS).The expression levels of iron metabolism related proteins were detected with Western blotting.Results In the T1DM group,significant declines in learning and memory abilities(P<0.01)and impaired neuronal morphology and reduced neuronal counts in the hippocampal CA1 region(P<0.01),CA3 region(P<0.01),and cortex(P<0.05)were observed when compared with those in the Ctrl group.ICP-MS analysis showed a marked increase in the hippocampal iron content in the T1DM group(P<0.01).Western blot results demonstrated that T1DM rats exhibited obviously up-regulated expression of iron storage proteins FTH and FTL in both the hippocampus(P<0.01,P<0.05)and the cortex(P<0.05,P<0.01),enhanced expression of iron import protein DMT1 in both the hippocampus and the cortex(P<0.05),while decreased expression of iron export protein FPN1 in the hippocampus(P<0.01)and the cortex(P<0.05).DFO treatment significantly ameliorated all above abnormalities.Conclusion The declines in learning and memory in T1DM rats are closely associated with neuronal damage induced by cerebral iron overload.Iron import protein DMT1 and export protein FPN1 jointly regulate cerebral iron content in T1DM rats.DFO reduces brain iron levels and mitigates iron overload-mediated neuronal injury by modulating the expression of DMT1 and FPN1.