Objective To construct a hypertension structured database based on Yi-9B large language model by aiming at the large amount of unstructured data generated in the process of hypertension diagnosis and treatment in order to elevate the efficiency of data management and provide the support for clinical deci-sion-making.Methods The key clinical informations of 114 369 patients with hypertension visiting in the Sec-ond Affiliated Hospital of Army Medical University during 2014-2023 were extracted.The Yi-9B large lan-guage model was used for conducting the entity identification and data structuring,and the database architec-ture was designed for statistical analysis and clinical application.Results After the database structuring process,the mean values of systolic and diastolic blood pressure were(149.98±20.55)mmHg and(86.90±13.75)mmHg,respectively.According to the classification of blood pressure level,the proportions of the nor-mal high value for high risk,very high risk of hypertension grade 1,and very high risk of hypertension grade 2 were the highest,which accounted for 20.73％,27.80％and 19.59％respectively.52.64％of the patients were complicated with heart disease,10.18％with complicating diabetes and 12.71％with complicating hy-perlipidemia.Logistic regression analysis showed that＞50-60 and＞60-70 years old was the high incidence age segment,moreover the systolic blood pressure showed an increasing trend with the age increase,reflecting the universality of hypertension in aging.This database significantly improved the efficiency of diagnosis and treatment in clinical application and realized the efficient analysis and management of data.Conclusion The hyper-tension structured database based on Yi-9B large language model effectively processes the unstructured data,significantly improves the efficiency of data extraction and management,helps to optimize the diagnosis and treatment decision-making,improves the management efficiency and provides the support for intelligent man-agement and personalized diagnosis and treatment.

The Solomon four-group design， a critical method for improving internal validity in clinical research， can reduce bias and control the interference of Hawthorne effects and pretest sensitization on research results， which offers unique advantages in evaluating complex intervention outcomes. This paper systematically outlined the core framework and key points of statistical analysis of the Solomon four-group design， summarized its applications in clinical research at home and abroad， explored its advantages and limitations， and discussed the potential value in traditional Chinese medicine （TCM） clinical trials. It is believed that the Solomon four-group design can help distinguish between testing effects and intervention effects in TCM clinical studies， and reduce the bias in the evaluation of subjective indicators. Meanwhile， given the complexity of the Solomon four-group design and the particularity of TCM clinical research， it is proposed that future TCM clinical studies should focus on using psychological scales， know-ledge， attitude， and behavior measurements， and other similat evaluations as endpoints. It also advocates strengthening interdisciplinary collaboration to provide new methodological paths for TCM clinical research.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

Abstract Migraine is a widespread neurovascular disorder, the pathogenesis of which is closely linked to the release of calcitonin gene-related peptide(CGRP), leading to a significant impairment in patients′ quality of life. CGRP receptor antagonists exert their therapeutic effect by inhibiting the interaction between CGRP and its receptors, thereby preventing migraine attacks. Currently, several agents, including Rimegepant and Ubrogepant, have either received approval from the U. S. Food and Drug Administration or are in advanced stages of clinical trials.These drugs offer multiple advantages, such as the absence of vasoconstrictive effects, a rapid onset of action, and minimal interference with the immune system. Nevertheless, further investigation is necessary to assess their longterm safety, the potential emergence of drug resistance, and the development of individualized treatment protocols.Moreover, the integration of these novel therapies with existing treatment strategies remains a critical area for future research. This review aims to summarize recent national and international scientific advancements to establish a theoretical basis for the application of precision medicine in migraine management.

AIM: To explore the effects of preoperative anterior segment parameters on the rotational stability of Toric intraocular lens(Toric IOL).METHODS:Prospective study. A total of 41 cataract patients(54 eyes)with combined corneal regular astigmatism from March to December 2023 were included and treated with cataract phacoemulsification combined with plate loop Toric IOL implantation in the Department of Ophthalmology of the First Affiliated Hospital of Zhengzhou University. The rotation degree of Toric IOL and uncorrected distance visual acuity(UCDVA)were evaluated at 1 d, 2 wk, and 1 mo postoperatively, the corrected distance visual acuity(CDVA)was evaluated at 2 wk and 1 mo after surgery, and the decentration and tilt of the Toric IOL were assessed at 2 wk postoperatively.RESULTS:A total of 33 patients(40 eyes)were included in this study. The UCDVA(LogMAR)of 1 d, 2 wk and 1 mo postoperatively were 0.10(0.10, 0.30), 0.05(0, 0.10)and 0(0, 0.10), respectively, which was improved compared with the preoperative levels of [0.80(0.49, 1.00)](P<0.001). The CDVA(LogMAR)of 2 wk and 1 mo postoperatively were 0.05(0, 0.15)and 0(0, 0.138), respectively, which was improved compared with preoperative levels of [0.52(0.40, 0.80)](P<0.001). The residual astigmatism of 2 wk and 1 mo postoperatively were 0.625(0.25, 0.75)D and 0.50(0.25, 0.75)D, respectively, which was significantly reduced compared with preoperative astigmatism of [1.82(1.31, 2.59)D](P<0.001). The preoperative anterior segment length(ASL), and lens thickness(LT)were positively correlated with Toric IOL rotation degree at 1 d(rs=0.463, P=0.003; rs=0.340, P=0.032)and 2 wk(rs=0.520, P=0.001; rs=0.409, P=0.009)postoperatively. At 1 mo postoperatively, only ASL was positively correlated with Toric IOL rotation degree(rs=0.463, P=0.003). The results of linear regression analysis showed that preoperative ASL was a predictor of rotation degree at 1 d, 2 wk and 1 mo after surgery(F1 d=10.098, P1 d=0.003; F2 wk=16.915, P2 wk<0.001; F1 mo=10.957, P1 mo=0.002). The rotation degree of Toric IOL was positively correlated with lens decentration(rs=0.360, P=0.043).CONCLUSION:The early postoperative rotation of Toric IOL is positively correlated with ASL, and the rotation is also positively correlated with lens decentration.

Generative Artificial Intelligence ishows a broad application prospect in the field of healthcare and has become an important technical means to promote the development of medical informatization.It addresses the multi-faceted challenges of medical record documentation,including efficiency,quality,and doctor-patient communica-tion.It analyzes the adaptability and feasibility of Generative Artificial Intelligence in different clinical scenarios of intelli-gent medical record generation.Additionally,it explores the issues present in current applications and proposes corre-sponding solutions,providing references for the effective application and continuous optimization of Generative Artifi-cial Intelligence in medical record documentation.This provides a theoretical foundation for further expanding the appli-cation scenarios of automatic medical record documentation in China's healthcare industry.

Generative Artificial Intelligence ishows a broad application prospect in the field of healthcare and has become an important technical means to promote the development of medical informatization.It addresses the multi-faceted challenges of medical record documentation,including efficiency,quality,and doctor-patient communica-tion.It analyzes the adaptability and feasibility of Generative Artificial Intelligence in different clinical scenarios of intelli-gent medical record generation.Additionally,it explores the issues present in current applications and proposes corre-sponding solutions,providing references for the effective application and continuous optimization of Generative Artifi-cial Intelligence in medical record documentation.This provides a theoretical foundation for further expanding the appli-cation scenarios of automatic medical record documentation in China's healthcare industry.

Objective To construct a risk prediction model for depression in the patients with posther-petic neuralgia(PHN)based on machine learning algorithm to provide a new idea and method for accurate prediction of depressive state occurrence in clinical PHN patients.Methods The inpatients with PHN in the Second Affiliated Hospital of Army Military Medical University from June 2022 to June 2023 were selected as the study subjects and randomly divided into the training set and test set according to the ratio of 8∶2,and whether or not the depressive state occurring served as the outcome variable.Based on six machine learning al-gorithms of K-Nearest Neighbor(KNN),Decision Tree(DT),Logistic Regression(LR),Naive Bayes(NB),Random Forest(RF)and Support Vector Machine(SVM),a risk prediction model for PHN patients with complicating depressive state was constructed.The model performance was evaluated based on the area under the curve(AUC),accuracy,precision,recall rate and F1 score,and the optimal model was selected.Results A total of 275 PHN patients were included,among them 170 cases developed the depressive state,and the inci-dence rate of depressive state was 61.82％.The AUC values of KNN,DT,LR,NB,RF and SVM models were 0.574,0.589,0.760,0.742,0.591 and 0.733,respectively,among which the AUC value,accuracy,precision,recall rate and F1 score of LR model were the highest.Conclusion The risk prediction model of PHN compli-cating depressive state based on LR machine learning algorithm has the best performance,which is helpful for early clinical assessment and prevention of depressive state.

The functional role of glutamatergic (vGluT2) neurons in the pedunculopontine nucleus (PPN) in modulating motor activity remains controversial. Here, we demonstrated that the activity of vGluT2 neurons in the rostral PPN is correlated with locomotion and ipsilateral head-turning. Beyond these motor functions, we found that these rostral PPN-vGluT2 neurons remarkably respond to salient stimuli. Furthermore, we systematically traced the upstream and downstream projections of these neurons and identified two downstream projections from these neurons to the caudal pontine reticular nucleus/anterior gigantocellular reticular nucleus (PnC/GiA) and the zona incerta (ZI). Our findings indicate that the projections to the PnC/GiA inhibit movement, consistent with 'pause-and-play' behavior, whereas those to the ZI promote locomotion, and others respond to a new 'pause-switch-play' pattern. Collectively, these findings elucidate the multifaceted influence of the PPN on motor functions and provide a robust theoretical framework for understanding its physiological and potential therapeutic implications.
Pedunculopontine Tegmental Nucleus/physiology* ; Animals ; Neural Pathways/physiology* ; Vesicular Glutamate Transport Protein 2/metabolism* ; Locomotion/physiology* ; Glutamic Acid/metabolism* ; Neurons/physiology* ; Male ; Mice ; Motor Activity/physiology* ; Zona Incerta/physiology*

Objectives:To examine the feasibility of using foundation model in computer vision for echocardiographic left ventricular ejection fraction measurement. Methods:Based on the most extensive publicly accessible repository of echocardiographic loops,EchoNet-Dynamic,featuring 10024 recordings from individual patients,a foundation model in computer vision,VideoMAE V2,was fine-tuned,validated,tested using 7460,1288,and 1276 echocardiographic loops,respectively. Results:The mean absolute error between left ventricular ejection fraction measurements of VideoMAE V2 and expert's measurements was 3.94％ (95％CI:3.79％-4.11％).The Pearson's correlation coefficient was 0.91 (95％CI:0.89-0.92).Additionally,VideoMAE V2 demonstrated exceptional accuracy in identifying patients with a left ventricular ejection fraction below 50％,achieving an AUC of 0.96 (95％CI:0.95-0.97). Conclusions:This study validates the feasibility of using foundation model in computer vision for measuring left ventricular ejection fraction in echocardiographic loops and lays the foundation for the development of a generalized multimodal automated interpretation system for echocardiography.