ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveThis study aimed to investigate the action mechanism by which Banxia Xiexintang （BXT） inhibits epithelial-mesenchymal transition （EMT） in chronic atrophic gastritis （CAG） rats by regulating the interleukin-17（IL-17）/extracellular regulated protein kinases（ERK）/CCAAT enhancer binding protein β（C/EBPβ）signaling pathway， thereby providing new theoretical evidence for the treatment of CAG with classic traditional Chinese medicine formulas. MethodsA CAG rat model was established by using the combined factor method. After successful modeling， the rats were randomly divided into the model group， low-， medium-， and high-dose groups （0.549， 1.098， 2.196 g·kg^-1， respectively） of BXT， and the positive drug group （vitacoenzyme， 0.3 g·kg^-1）. A normal control group was also set up. After 8 weeks of intervention， the pathological changes of gastric tissue were evaluated. The enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of IL-17， tumor necrosis factor-α （TNF-α）， cyclooxygenase-2 （COX-2）， and C/EBPβ in serum， as well as the contents of EMT markers in gastric mucosal tissue including E-cadherin， N-cadherin， and vimentin. The immunohistochemistry method was employed to determine the localization and protein expression levels of IL-17， p-ERK， and C/EBPβ in gastric mucosal tissue. Western blot was used to detect the protein expressions of C/EBPβ， ERK， and its phosphorylated form （p）-ERK in gastric mucosa. Real-time polymerase chain reaction （Real-time PCR） was applied to measure the mRNA expression levels of ERK， COX-2， and C/EBPβ in gastric mucosa. ResultsCompared with those in the normal control group， the rats in the model group showed gastric mucosal glandular atrophy and inflammatory cell infiltration. The protein and their related mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly increased （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBPβ in serum were significantly increased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosal tissue were significantly increased， while the content of E-cadherin was significantly decreased （P<0.01）. Compared with the model group， after intervention with different doses of BXT， the pathological damage of the gastric mucosa was improved to varying degrees. The protein and mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly reduced （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBP β in serum were significantly decreased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosa tissue were decreased， while the content of E-cadherin was increased （P<0.05，P<0.01）. ConclusionBXT can effectively improve the pathological damage of gastric mucosal tissue in CAG rats. Its action mechanism may be related to reducing the levels of IL-17 and TNF-α in serum， regulating the IL-17/ERK/C/EBPβ signaling pathway and inhibiting the EMT process.

Objective To systematically review international research on health communication, and to provide valuable insights and reference for China's health communication research and practice. Methods This study included 693 articles published from January 2023 to April 2024 in two authoritative academic journals in the field of health communication, ^“Health Communication^” and the ^“Journal of Health Communication^”. A systematic review was conducted on the themes, theoretical foundations, research methods, and populations of international health communication research. Results The findings in this study revealed that international health communication research topics were diverse, with hotspots including social media, health information behavior, health misinformation, stigmatization, trust, and risk perception. The results showed that 34% of the articles were based on theoretical foundations, and 93.3% employed research methods, focusing on adolescents, parents, women, and other key populations. Conclusion Domestic health communication research can expand its perspective from ^“information transmission^” to ^“social interaction^”, innovate theories and methods from ^“single paradigm" to ^“multi-integration^” and shift focus from a ^“mass perspective^” to ^“targeted care^” for the health of all populations. Domestic health communication practice can delve into the localization of social media health communication practices, the comprehensive management of health misinformation, and the critical application of new technologies.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

ObjectiveTo analyze the research status and development trends of non-pharmacological therapies for post-stroke spasticity (PSS) over the past two decades. MethodsRelevant literatures on non-pharmacological rehabilitation of PSS published from January, 2004 to June, 2024 were retrieved from Web of Science Core Collection. CiteSpace 6.3.R6 and VOSviewer 1.6.18 were used for visualization analysis. ResultsA total of 780 publications were included. The annual number of publications showed an overall upward trend. China, the USA, and Italy contributed the highest number of publications. The Hong Kong Polytechnic University and researcher Noureddin Nakhostin Ansari were identified as the most influential institution and author, respectively. High-frequency keywords and cluster labels included electric stimulation, transcranial magnetic stimulation, robot and acupuncture. ConclusionOver the past 20 years, researches on non-pharmacological therapies for PSS have remained active, with hotspots focusing on diverse interventions such as electrical stimulation, magnetic stimulation and robot-assisted therapy.

[Objective] To investigate the seroprevalence characteristics of hepatitis E virus (HEV) among blood donors with hepatitis B virus (HBV) infection, so as to provide data support for the monitoring, prevention and treatment of HEV. [Methods] From January to December 2022, 219 samples positive for hepatitis B surface antigen (HBsAg), 142 occult hepatitis B virus infection (OBI) samples (HBV group) and 873 samples tested negative (control group) were collected. 361 samples were further tested with viral load assay and serological testing for five serological markers (HBsAg, HBsAb, HBeAg, HBeAb and HBcAb), and the DNA load was measured using real time fluorescence quantitative PCR. Commercially available enzyme-linked immunosorbent assays (ELISA) were used for the detection of anti-HEV IgG, anti-HEV IgM and HEV antigen (Ag). The Chi-square test or Fisher's exact test was used to assess the differences in the reactivity rates of anti-HEV IgG and anti-HEV IgM among different blood donor populations and different variables. Multivariable logistic regression was used to examine potential risk factors associated with anti-HEV IgG seroprevalence. [Results] In the HBV group, HBsAg positive donors exhibited low expression of antigen. The HBV DNA load of OBI infected donors ranged from 1 to 131.43 IU/mL (median 11.24 IU/mL). The prevalence of anti-HEV IgG and IgM antibody in the HBV group were 34.63% and 1.11%, respectively. Among them, the prevalence of anti-HEV IgG and anti-HEV IgM in the HBV group was 34.63% and 0, respectively (P<0.05), while in the OBI donors, they were 41.55% and 2.82%, respectively. In the normal donors, the reactivity rates for anti-HEV IgG and anti-HEV IgM were 18.67% and 1.49%, respectively. Statistical analysis showed that there was a difference in the reactivity rate of anti-HEV IgG between the HBV-infected donors and the normal donors (34.63% vs 18.67%, P<0.05), but no difference in the reactivity rate of anti-HEV IgM (1.11% vs 1.49%, P>0.05). No HEV Ag was detected in either group of blood donors. Multivariate logistic regression analysis indicated that age was an independent risk factor for anti-HEV IgG reactivity in both groups of blood donors. [Conclusion] The reactivity rate of anti-HEV IgG among HBV-infected blood donors was significantly higher than that in the normal donors in Wuhan, with age being an independent risk factor. Therefore, for HBV-infected donors, it is essential to strengthen and prioritize the prevention and treatment of HEV to reduce the spread of HEV.

Objective:To investigate the current status of knowledge, attitude, and practice in career planning among medical students and its influencing factors, and to facilitate the education of medical career planning.Methods:The convenience sampling method was used to distribute a self-made questionnaire, and related data were gathered from 295 medical students in Wuhan, China. SPSS 26.0 was used to perform analyses of related categorical variables, including descriptive statistics, univariate tests, rank-sum tests, and the binary logistic regression analysis.Results:The results showed that the medical students with good performance of career planning knowledge, attitude, and practice accounted for 68.48%, 87.12%, and 54.92%, respectively. Major, grade, professional satisfaction, and professional learning objectives were influencing factors for career planning knowledge among the medical students ( P<0.05); the intention for laboratory participation, the basis for major selection, and professional learning objectives were influencing factors for career planning attitude ( P<0.05); grade, internship experience, and professional learning objectives were influencing factors for career planning practice ( P<0.05). Conclusions:Related measures should be adopted to strengthen career planning education for medical talents, such as perfecting the whole-process and multi-agent career planning guidance system, stimulating the enthusiasm of students, and clarifying professional learning objectives.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.