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.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

Objective To investigate the incidence and adverse prognosis of late onset sepsis(LOS)in neonates in neonatal intensive care unit(NICU).Methods A retrospective study was conducted to collect and analyze the peri-natal condition,underlying diseases,invasive procedures,and adverse prognosis of neonates in NICU of a regional maternal and child healthcare hospital from 2019 to 2023.According to whether LOS occurred during hospitaliza-tion,neonates were divided into LOS group and non-LOS group.The LOS group was divided into 5 subgroups based on whether invasive procedures were performed:LOS plus umbilical vein catheter(UVC)group,LOS plus peripherally inserted central catheter(PICC)group,LOS plus sequential catheter group,LOS plus tracheal intuba-tion group,and LOS plus lumbar puncture group,the relationship between LOS and adverse prognosis was ana-lyzed.Results Among 2 945 neonates in NICU,354(12.02％)developed LOS.Comparison between LOS groups and non-LOS group were as follows:in term of perinatal condition of neonates,there were statistically significant difference in weight,gestational age,and whether they were twins between the two groups(all P＜0.001);in term of underlying diseases,there were statistically significant differences in the number of cases of maternal gestational hypertension,neonatal asphyxia,neonatal congenital heart disease,neonatal ventricular dilation,neonatal pneumo-nia,neonatal hyperthyrotropinemia,and neonatal anemia,as well as five invasive procedures between the two groups(all P＜0.05).Compared with the non-LOS group,the incidences of retinopathy of prematurity(ROP),neonatal necrotizing enterocolitis(NNEC),bronchopulmonary dysplasia(BPD),and neonatal respiratory distress syndrome(NRDS)in LOS group were all higher(all P＜0.001).Regression analysis showed that compared with the non-LOS groups,the risk of ROP increased in the LOS group and its subgroups,with the LOS plus sequential catheter group having a 2.27-fold higher risk of ROP than non-LOS group;the risk of NNEC increased in the LOS group and its subgroups,with the LOS plus UVC group having an 8.29-fold higher risk of NNEC than the non-LOS group.Except for the LOS plus UVC group,the risk of BPD increased in the LOS group and other subgroups,with the LOS plus PICC group and LOS plus sequential catheter group having 4.68-and 4.64-fold higher risk of BPD than the non-LOS group,respectively;the risk of NRDS in the LOS plus PICC group was 6.84-fold higher than the non-LOS group(all P＜0.05).The top three pathogens causing LOS were coagulase negative Staphylococcus,Klebsiella pneumoniae,and Escherichia coli.Conclusion LOS can significantly increase the risks of ROP,NNEC,BPD,and NRDS.LOS plus invasive procedures can further increase the risk of adverse prognosis.

Aim To investigate the effect of Rtv(a P-gp inhibitor and inducer)on the pharmacokinetics of Y101(P-gp substrate)via P-gp.Methods In short-term studies,rats received a single dose of Rtv,where-as in long-term studies they received continuous dosing for seven days.Following this treatment,Y101 was o-rally administered to analyze its blood concentration in rats.Subsequently,the mechanism by which Rtv af-fected Y101 pharmacokinetics was investigated through the everted gut sac model(in vitro),cellular uptake studies,and so on.Results Short-term administra-tion of Rtv significantly increased Y101's AUC,liver-to-plasma partition coefficient,the everted gut sac model(in vitro),and cellular accumulation.Although long-term Rtv treatment had no effect on Y101 pharma-cokinetics or hepatic distribution,it markedly reduced Y101 cellular accumulation in Caco-2 cells,concomi-tant with an upregulation of P-gp expression.Conclu-sions Short-term Rtv administration acts as a compet-itive P-gp inhibitor,enhancing Y101 intestinal absorp-tion and hepatic distribution.In contrast,the plasma pharmacokinetics and hepatic distribution of Y101 are not altered after long-term administration of Rtv,po-tentially attributable to Rtv's dual modulatory effects on P-gp involving both induction and inhibition.Hence,the potential Rtv and Y101 interaction should be close-ly monitored in the clinic.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

AIM To explore the clinical effects of Jiawei Yanghe Decoction combined with Budesonide and Formoterol Fumarate Powder for Inhalation on patients with mild to moderate bronchial asthma in chronic and persistent period.METHODS One hundred and eighteen patients were randomly assigned into control group(59 cases)for 4-week administration of Budesonide and Formoterol Fumarate Powder for Inhalation,and observation group(59 cases)for 4-week administration of both Jiawei Yanghe Decoction and Budesonide and Formoterol Fumarate Powder for Inhalation.The changes in clinical effects,ACT score,bronchial asthma control rate,pulmonary function indices(FEV1,PEF,FEV1％,PEF％),inflammatory indices(EOS,EOS％,FeNO),TCM syndrome score and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed increased bronchial asthma control rate,ACT score,PEF(P＜0.05),and decreased TCM syndrome score(P＜0.05),especially for the observation group(P＜0.05);the observation group exhibited increased FEV1,FEV1％,PEF％(P＜0.05),among which FEV1,PEF％were higher than those in the control group(P＜0.05);the observation group showed decreased inflammatory indices(P＜0.05),among which FeNO was lower than that in the control group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with mild to moderate bronchial asthma in chronic and persistent period,Jiawei Yanghe Decoction combined with Budesonide and Formoterol Fumarate Powder for Inhalation can safely and effectively alleviate clinical symptoms,improve pulmonary functions,airway inflammatory reactions,and enhance bronchial asthma control rate.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

AIM To explore the clinical effects of Jiawei Yanghe Decoction combined with Budesonide and Formoterol Fumarate Powder for Inhalation on patients with mild to moderate bronchial asthma in chronic and persistent period.METHODS One hundred and eighteen patients were randomly assigned into control group(59 cases)for 4-week administration of Budesonide and Formoterol Fumarate Powder for Inhalation,and observation group(59 cases)for 4-week administration of both Jiawei Yanghe Decoction and Budesonide and Formoterol Fumarate Powder for Inhalation.The changes in clinical effects,ACT score,bronchial asthma control rate,pulmonary function indices(FEV1,PEF,FEV1％,PEF％),inflammatory indices(EOS,EOS％,FeNO),TCM syndrome score and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed increased bronchial asthma control rate,ACT score,PEF(P＜0.05),and decreased TCM syndrome score(P＜0.05),especially for the observation group(P＜0.05);the observation group exhibited increased FEV1,FEV1％,PEF％(P＜0.05),among which FEV1,PEF％were higher than those in the control group(P＜0.05);the observation group showed decreased inflammatory indices(P＜0.05),among which FeNO was lower than that in the control group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with mild to moderate bronchial asthma in chronic and persistent period,Jiawei Yanghe Decoction combined with Budesonide and Formoterol Fumarate Powder for Inhalation can safely and effectively alleviate clinical symptoms,improve pulmonary functions,airway inflammatory reactions,and enhance bronchial asthma control rate.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.