Objective To explore the risk factors of progression to dementia within 2 years in patients with recent subcortical small infarction (RSSI) complicated with cognitive dysfunction. Methods A total of 340 patients with RSSI complicated with cognitive dysfunction who were treated in the hospital and completed 2-year follow-up were selected from February 2021 to February 2025. According to whether the patients progressed to dementia, they were classified into dementia group (n=105) and non-dementia group (n=235). The clinical data were compared between both groups, and the independent risk factors were screened by Logistic regression analysis. Results Multivariate logistic regression analysis suggested that history of hypertension (OR=1.919), history of diabetes mellitus (OR=1.597), multiple infarctions (OR=1.455), severe white matter lesions (OR=1.595), no cognitive function training (OR=1.923), increased infarct size (OR=1.069), reduced MMSE score (OR=0.945) and increased levels of NfL (OR=1.049) and IL-6 (OR=1.038) were independent risk factors for the progression to dementia (all P<0.05). Conclusion The progression to dementia in patients with recent subcortical small infarction and cognitive dysfunction is affected by multiple factors. In clinical practice, the integration of vascular risk factors, imaging features, cognitive assessment and serum biomarkers (NfL, IL-6) helps to construct an early risk prediction model and implement targeted interventions for high-risk groups.

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.

Aim To investigate the mechanism of 8-hydroxygenistein(8-OHG)in mitigating high-altitude induced heart injury(HAHI)via network pharmacolo-gy,molecular docking and animal experiment.Meth-ods 8-OHG-related targets were obtained from Swis-sTargetPrediction,Similarity ensemble approach,Su-perPred and PharmMapper databases.Genecards and OMIM databases were utilized for retrieving HAHI-re-lated targets.Venn diagram was drawn using R pack-age.STRING 11.5 and Cytoscape 3.9.1 were used to construct the protein-protein interaction network and screen core targets.GO and KEGG enrichment analysis were carried out using DAVID database.AutoDock Vi-na software was used for molecular docking.Visualiza-tion was performed using PyMOL 3.0.0 software.The HAHI model was established,and the the mice were randomly divided into the control group,model group and 8-OHG group.Hematoxylin-eosin(HE)staining was used to observe the pathological changes of myo-cardial tissue.Western blot was applied for detecting the expression levels of related proteins in myocardial tissue.Results A total of 73 overlapping targets be-tween 8-OHG and HAHI were screened,with ALB,AKT1,ESR1,HSP90AA1,NFKB1 and MMP9 were regarded as core targets.Molecular docking results in-dicated that 8-OHG had strong binding ability with these core targets.GO functional enrichment analysis obtained 185 biological processes,including negative regulation of apoptosis,response to hypoxia and in-flammatory response,38 cell compositions,including cytosol,cytoplasm,plasma membrane,as well as 71 molecular functions,including protein binding,metal ion binding,enzyme binding and so on.Altogether 55 signaling pathways were identified via KEGG enrich-ment analysis,including PI3 K/Akt signaling pathway,HIF-1 signaling pathway and MAPK signaling pathway.The results of animal experiments showed that 8-OHG could significantly improve the myocardial histopatho-logical change induced by high-altitude hypoxia expo-sure.Western blot results showed that compared with the normal group,the ratio of p-PI3K/PI3K and p-Akt/Akt in the myocardial tissue of mice in the model group significantly decreased,while the protein expres-sion of Beclin-1 and the ratio of LC3B-Ⅱ/LC3B-Ⅰsignificantly increased,while 8-OHG could reverse these changes.Conclusion The mechanism of 8-OHG in alleviating HAHI is related to its activation of PI3K/Akt signaling pathway,thereby inhibiting auto-phagy induced by high-altitude hypoxia exposure.

Aim To explore the mechanism of action of Guizhi Jia Gegen decoction(GGD)in treating pneu-monia-enteritis induced by H1N1 influenza virus infec-tion in a mouse model,using network pharmacology and molecular docking techniques,followed by in vivo verification.Methods A pneumonia-enteritis mouse model was established,and the intervention effects of GGD on the model mice were evaluated using indica-tors such as body weight,rectal temperature,lung in-dex,colon length,H1N1 M gene expression,relative mRNA expression levels of inflammatory cytokines,and pathological sections of the lung and intestine.The targets of the blood-absorbed components of GGD were identified using the Swiss Target Prediction platform,and the disease targets were retrieved from the Gene-Cards platform.The intersecting targets were analyzed through PPI network analysis using the STRING data-base to identify core targets.GO analysis and KEGG pathway enrichment analysis were performed using the Metascape database.RT-qPCR was employed to vali-date the core targets and pathways.Molecular docking was conducted using AutoDock Tools software to verify the interactions between blood-absorbed components and key targets.Results GGD demonstrated signifi-cant therapeutic effects on the pneumonia-enteritis mouse model.The results of network pharmacology in-dicated that the therapeutic effects of GGD were strong-ly associated with targets such as TNF,ALB,PTGS2,MMP9,EGFR,ESR1,SRC,HSP90AA1,PPARG and MMP2.RT-qPCR results indicated that GGD could intervene in pneumonia-enteritis by regulating the targets TNF,ALB,EGFR and the related targets of the NF-κB pathway.Molecular docking results re-vealed that blood-absorbed components such as puerar-in and liquiritin could stably bind to TNF,ALB and EGFR.Conclusion Components such as puerarin and liquiritin in GGD may exert therapeutic effects on pneumonia-enteritis induced by H1N1 influenza virus infection by acting on targets such as TNF,ALB and EGFR.

Aim To evaluate the role of the glucose transporter protein 1(GLUT1)-dependent glycolytic in the attenuation of oxygen-glucose deprivation-reoxygen-ation(OGD/R)injury in HK-2 cells by dexmedetomi-dine(Dex).Methods C57/BL6 mice were random-ly divided into three groups(n=6),namely,sham operation group(Sham group),renal ischemia reper-fusion group(I/R group)and Dex group(I/R+Dex group).Serum creatinine(Cr)and urea nitrogen(BUN)were measured,while the levels of key glyco-lytic enzymes HK2,PFKFB3 and GLUT1 were meas-ured.HK-2 cells were cultured and randomised into seven groups(n=6),which was treated with OGD/R,overexpression or interference with GLUT1,Dex and glycolysis inhibitor 2-DG.CCK-8 and LDH activi-ty were used to detect cellular damage.Glycolysis lev-els were detected by lactate and ECAR.The inflamma-tory level was reflected by qRT-PCR for IL-6 and TNF-α.qRT-PCR and Western blot were performed to de-tect the levels of GLUT1,HK2,and PFKFB3.Results Dex significantly ameliorated kidney injury and HK-2 cell injury(P＜0.05).Dex inhibited the OGD/R-induced rise in lactate and extracellular acidification rate(ECAR),as evidenced by suppression of the ex-pression of GLUT1,HK2 and PFKFB3(P＜0.05).In vitro experiments showed that GLUT1 knockdown sig-nificantly improved OGD/R-induced cellular damage.Lactate,ECAR,glycolysis-related mRNAs and pro-teins were inhibited by GLUT1 knockdown(P＜0.05).Significantly,there were no significant differ-ences in above indexes after Dex treatment based on GLUT1 knockdown.Overexpression of GLUT1 abroga-ted the protective effects of Dex,while reversing the inhibitory effects of Dex on the expression of GLUT1,HK2,and PFKFB3(P＜0.05).Conclusions Dexmedetomidine attenuates OGD/R induced injury in HK-2 cells by inhibiting GLUT1-dependent glycolysis.

Objective To evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective To explore the potential categories of health risk behaviors among students aged 11 to 18 years in Minhang District,Shanghai in order to identify the influencing factors of health risk behaviors among different categories of students.Methods Using stratified cluster sampling techniques,a random selection of 1 690 middle school students from Minhang District,Shanghai,were chosen as the study sample.Data was collected using the"Shanghai Adolescent Health-Related Behavior Survey Questionnaire"to assess participants'scores on six categories of health risk behaviors.Latent profile analysis(LPA)was employed to cluster students'health risk behaviors,and a multifactorial Logistic regression model was used to analyze the related influencing factors.Results The health risk behaviors of students aged 11 to 18 in Minhang District,Shanghai,were categorized into 3 groups:Class 1,comprising 915 students(54.14%),with moderate to low scores in health risk behaviors,named"the group with moderately low levels of various health risk behaviors";Class 2,comprising 539 students(31.89%),with higher scores in psychological addiction,named"the group with psychological addiction as the prominent health risk behavior";Class 3,comprising 236 students(13.97%),with higher scores in substance addiction,named"the group with substance addiction as the prominent health risk behavior".The results of the univariate analysis for the three potential categories of student health risk behaviors show that there are statistically significant differences in the 3 potential categories of health risk behaviors among students of different schools,ages,ethnicities,and boarding situations(P<0.05).The analysis results of the multivariate Logistic regression indicate that compared to junior high school students,both senior high school students and vocational high school students have a higher risk of exhibiting unhealthy behaviors characterized by psychological addiction and substance addiction,with all differences being statistically significant(P<0.05).Conclusion The latent profile analysis reveals three distinct categories of health risk behaviors among students aged 11 to 18 years in Minhang District,Shanghai,with significant distribution differences among students from diverse backgrounds.Consequently,it is recommended that tailored health education and intervention measures be implemented for students with different school characteristics and at various educational stages.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.