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.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Objective To explore the changes in neural activity in patients with type 2 diabetes mellitus(T2DM)and their corre-lation with executive function,and to analyze the neural mechanisms underlying the decline in executive function in T2DM patients.Methods Thirty-one T2DM patients(T2DM group)and thirty-two healthy controls(HC)(HC group)matched for body mass index(BMI)underwent resting-state functional magnetic resonance imaging(rs-fMRI)scans and N-back task tests were included.Differ-ences in the amplitude of low-frequency fluctuation(ALFF),regional homogeneity(ReHo),and seed-based functional connectivity(FC)between the two groups were compared,and partial correlation analyses were performed between the difference results and N-back task performance.Results The T2DM group showed prolonged reaction time(RT)in the 1-back and 2-back tasks.T2DM patients exhibited increased ALFF in the bilateral caudate nucleus,left medial superior frontal gyrus,and right postcentral gyrus,as well as elevated ReHo in the right putamen.FC analysis revealed significant alterations in FC between the caudate nucleus,putamen,and multiple brain regions in T2DM patients,with some of these FC changes significantly correlated with RT and accuracy(ACC)in the N-back task.Conclusion The decline in executive function in T2DM patients may be associated with abnormal neural activity in brain regions such as the striatum,salience network,and frontoparietal control network.FC further decreases under increased cognitive load.These findings provide evidence for the study of the neural mechanisms of executive function impairment in T2DM patients.

Objective A non-targeted metabolomics analysis of vitreous fluid from patients with proliferative diabetic retinopathy(PDR)is conducted to explore the"metabolic map"of PDR.This approach aims to deepen the understanding of the disease,identify potential biomarkers.Methods From 35 PDR patients and 30 fresh rhegmatogenous retinal de-tachment(RRD)patients,10 PDR patients with vitreoretinal abnormal adhesions were selected as the experimental group(PDR group),and 10 fresh RRD patients were chosen as the control group(RRD group).Using ultra-high-performance liq-uid chromatography-mass spectrometry non-targeted metabolomics technology,the metabolic profiles of vitreous fluid were analyzed to obtain metabolic spectra.One-dimensional and multidimensional statistical methods were used to analyze the differences in metabolites and metabolic pathways between the PDR and RRD groups.Results A total of 165 differential metabolites were identified in the vitreous humor samples of patients in the PDR and RRD groups,these differential metab-olites were significantly enriched in 21 metabolic pathways(P＜0.05),Among these pathways,those with at least 5 differ-ential metabolites include:methionine and cysteine metabolism;glycine,serine,and threonine metabolism;ascorbic acid and aldose metabolism;amino acid biosynthesis;and central carbon metabolism in cancer.Pyruvate,serine,D-2-phospho-glycerate,threonine,phosphoserine,and high serine are present in multiple metabolic pathways,the areas under the curve are 0.96,0.82,0.85,0.78,0.40,and 0.31,respectively.Conclusion There are 21 significantly different metabolic pathways between PDR and RRD patients.Pyruvate stands out in multiple pathways,potentially serving as a biomarker for PDR diagnosis.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To investigate the effect of resveratrol on airway remodeling induced by ovalbumin(OVA)in asthmatic young mice based on the sphingosine-1-phosphate(S1P)/transforming growth factor-β1(TGF-β1)pathway.Methods Young mice were randomly assigned into normal group,model group,S1P group,resveratrol group,and resveratrol+S1P group,with 12 mice in each group.A mouse model of asthma induced by OVA was established.The Wright Giemsa staining was applied to detect the number of inflammatory cells in bronchoalveolar lavage fluid(BALF).ELISA was applied to detect the levels of interleukin-4(IL),IL-5,and IL-13 in BALF.The colorimetric method was applied to detect the activities of SOD and CAT and the content of MDA in lung tissue.HE staining was applied to observe pathological changes in lung tissue,and the wall area of bronchial tube(Wat)and perimeter of basement membrane(Pbm)were analyzed.Masson staining was applied to observe the area of collagen fibers in lung tissue.Immunohistochemistry was applied to detect the expression of α-SMA in lung tissue.Western blot was applied to detect the expression of E-cadherin,N-cadherin,Snail,SP1,and TGF-β1 proteins in lung tissue.Results Compared with the normal group,the number of eosinophils,lymphocytes,neutrophils,levels of IL-4,IL-5,and IL-13 in BALF,lung tissue MDA content,Wat/Pm ratio,collagen fiber area,α-SMA positive expression,the protein levels of N-cadherin,Snail,SP1,TGF-β1 increased in the model group,while the activities of SOD,CAT,and the protein level of E-cadherin in lung tissue decreased(P＜0.05).Compared to the model group,the number of eosinophils,lymphocytes,neutrophils,levels of IL-4,IL-5,and IL-13 in BALF,lung tissue MDA content,Wat/Pm ratio,collagen fiber area,α-SMA positive expression,the protein levels of N-cadherin,Snail,SP1,TGF-β1 decreased in the resveratrol group,while the activities of SOD,CAT,and the protein level of E-cadherin in lung tissue increased(P＜0.05).The S1P recombinant protein could weaken the inhibitory effect of resveratrol on airway remodeling in asthmatic young mice.Conclusion Resveratrol can inhibit oxidative stress,airway inflammation,and epithelial mesenchymal transition,thereby suppressing airway remodeling in asthmatic young mice.Its effect is related to the inhibition of the S1P/TGF-β1 signaling pathway.

Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective To investigate the early postoperative efficacy between Kahook dual blade(KDB)internal tra-beculectomy and internal gonioscopy-assisted transluminal trabeculotomy(GATT)in the treatment of primary open-angle glaucoma(POAG).Methods A total of 120 patients(120 eyes)with POAG were included and divided according to dif-ferent surgical treatment methods.Among them,62 patients(62 eyes)undergoing internal trabeculectomy by KDB were di-vided into Group A,and 58 patients(58 eyes)undergoing GATT were divided into Group B.Relevant data of patients in the two groups were collected,and the efficacy of the two surgical methods was compared.Results The intraocular pres-sure in both groups decreased 1 day,1 week,and 1 month after surgery compared with preoperative levels(all P＜0.05).However,there was no significant difference in the intraocular pressure measured at the above time points between the two groups(all P＞0.05).The type of drugs for reducing the intraocular pressure in both groups decreased significantly 1 day,1 week,and 1 month after surgery compared with preoperative levels(all P＜0.05).However,there was no significant difference in the type of drugs for reducing the intraocular pressure at the above time points between the two groups(all P＞0.05).There was no statistically significant difference in the total incidence of complications between Group A(45.16％)and Group B(39.66％)(P＞0.05).Specifically,the main complications in Group A included anterior chamber hemorrhage and intraocular pressure rebound,while those in Group B included anterior chamber hemorrhage and ciliary detachment.Conclusion Both KDB internal trabeculectomy and GATT can effectively reduce the intraocular pressure in patients with POAG and reduce the use of postoperative intraocular pressure-lowering drugs.There was no significant difference in surgical complications between the two surgical methods.The selection of surgical procedures in clinical prac-tice can be determined based on patients' wishes and actual situation.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

Objective:To evaluate the effect of preoperative anxiety on the consciousness and autonomic nervous activity during propofol anesthesia.Methods:This study was a secondary analysis of data from the clinical trial in a prospective single-arm study. One hundred and thirty patients, aged 18-65 yr, with a body mass index of 18.5-27.9 kg/m 2, of American Society of Anesthesiologists Physical Status classification I or Ⅱ, scheduled to receive propofol anesthesia, were selected from the Second People′s Hospital of Lianyungang. The six-item of the state anxiety inventory (SAI) of the State-Trait Anxiety Inventory was used to assess the anxiety of patients 1 h before surgery. The patients were divided into 2 groups according to the cut-off value of 12: obvious anxiety symptom (SAI score >12) group (group A, n=49) and no obvious anxiety symptom (SAI score ≤12) group (group B, n=81). After admission to the operating room, the patient was required to hold a 50 ml syringe filled with water. Propofol was given by target-controlled infusion (TCI) with the target plasma concentration set at 5 μg/ml. When the effect-site concentration (Ce) of propofol increased to 3.5 μg/ml (all the patients lost consciousness), the closed-loop TCI was used to maintain BIS value between 45 and 55. The patients were monitored for 20 min after stopping the pump infusion (anesthesia recovery period). The disappearance time of verbal command, disappearance time of eyelash reflex, time of syringe dropping, recovery time of verbal command, recovery time of eyelash reflex, Ce at the recovery of verbal command, Ce at the recovery of eyelash reflex, Ce within the first 5 min of the closed-loop TCI, and consumption of propofol during anesthesia were recorded. The peripheral perfusion index, low frequency power and high frequency power of heart rate variability were recorded, and the ratio of low frequency power to high frequency power was calculated. Pearson correlation analysis was used to assess the correlation between preoperative SAI score and consciousness-related indicators, simulated Ce of propofol and consumption of propofol. Results:Compared with group B, the disappearance time of verbal command, disappearance time of eyelash reflex, and time of syringe dropping were significantly prolonged, the consumption of propofol, simulated Ce at recovery of verbal command and within the first 5 min of closed-loop TCI were increased, the peripheral perfusion index was decreased at each time point before administration and at 14-20 min of anesthesia recovery, and the low-frequency power was decreased during anesthesia maintenance in group A ( P<0.05). The SAI score was positively correlated with the disappearance time of verbal command ( r=0.220, P=0.012), time of syringe dropping ( r=0.206, P=0.029), consumption of propofol ( r=0.330, P<0.001), and the simulated Ce at the recovery of verbal command ( r=0.215, P=0.015) and simulated Ce at recovery of eyelash reflex ( r=0.207, P=0.022). Conclusions:Preoperative anxiety may lead to prolonged loss of consciousness and more marked inhibition of sympathetic nerve activity during propofol anesthesia.