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.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective:To clarify the changes in incidence and mortality of various cancers based on analysis on registration data of malignant tumor incidence and mortality from Dehui City and Yanji City in Jilin Province.Methods:The incidence and mortality data of malignant tumors from 2009 to 2016 in Dehui City and Yanji City in Jilin Province,were collected from the Chinese Cancer Registry Annual Report published by the National Cancer Center.The number of cases,deaths,crude incidence rate,crude mortality rate,age-standardized incidence rate(ASIR),age-standardized mortality rate(ASMR),and annual percentage change(APC)of the malignant tumors were analyzed by cancer sites and genders.Results:From 2009 to 2016,the CIR of malignant tumors in Dehui City(APC=1.2%,P=0.019)and Yanji City(APC=3.6%,P=0.058)showed an increasing trend.After standard population age adjustment,the ASIR in males in Dehui City showed a significant decline(APC=-5.7%,P=0.021),while the ASIR in females exhibited an overall downward trend,but the difference was not significant(APC=-2.2%,P=0.111).In Yanji City,the ASIR in males(APC=-1.4%,P=0.535)and females(APC=0.0%,P=0.988)showed no significant changes.The CMR of malignant tumors in Dehui City(APC=1.9%,P=0.001)and Yanji City(APC=5.9%,P=0.001)showed a continuous upward trend.After age-standardization,the ASMR in males(APC=-3.1%,P=0.100)and females(APC=-4.2%,P=0.053)in Dehui City,as well as in males(APC=-1.3%,P=0.438)in Yanji City,showed a slight downward trend.Although the ASMR in females in Yanji City showed a slight increase,the difference was not statistically significant(APC=0.5%,P=0.838).In 2016,the most common malignant tumor in terms of both incidence and mortality in Dehui City was lung cancer,with a CIR of 60.76/100 000 and a CMR of 46.96/100 000.In Yanji City,the most common malignant tumor was liver cancer,with a CIR of 49.04/100 000 and a CMR of 51.09/100 000.Conclusion:Lung cancer,liver cancer,and gastric cancer are the major malignant tumors threatening residents in Dehui City,Yanji City,and even the entire Jilin Province,and should be prioritized in cancer prevention and control efforts.Early diagnosis and treatment should be strengthened.

Organophosphorus nerve agents are the most threatening chemical warfare agents and terrorist agents.The number of nerve agents and their related chemicals involved in the verification of Chemical Weapon Convention(CWC)exceeds ten million,with the majority being isomers.Accurate structural identification of these chemicals has always been one of the challenges in CWC related verification analysis.In this work,a total of 17 kinds of alkyl phosphonate isomers and structural analogs from 5 groups were designed and synthesized,and then analyzed by gas chromatography-mass spectrometry(GC-MS)and gas chromatography-infrared spectroscopy(GC-FTIR).The spectra of isomers or structural analogs obtained from two techniques as well as the structural information provided therein were compared and analyzed.The results showed that for isomers or structural analogs with similar MS spectra,FTIR spectra could provided more structural fingerprint information of compounds and had advantages in confirming structures.Combined with the excellent separation ability of GC,GC-FTIR can be used to assist GC-MS in the structural confirmation of alkyl phosphates,achieving rapid and accurate identification of isomers or structural analogues.

Insulin is an important protein hormone that participates in multiple metabolic pathways.Biosynthetic insulin has been widely used in the treatment of type 1 and type 2 diabetes.Currently,the number of reported cases of insulin overdose both at home and abroad is gradually increasing,and insulin homicide is no longer a means of"committing murder without leaving a trace".At present,there are no systematic protocols for the identification of insulin overdose in the field of forensic medi-cine in China.This article introduces the causes,toxicological characteristics,forensic examination,labo-ratory testing methods and indicator reference of insulin overdose.Based on the identification practice and research results and referring to relevant studies on insulin overdose at home and abroad,this pa-per aims to provide recommendations and references for the formulation of forensic identification guide-lines for insulin overdose cases.

OBJECTIVE: To precise classify sacral meningeal cysts, effective guide minimally invasive neurosurgery and postoperative personalized rehabilitation by multiple dimensions radiographic reconstruction MRI. METHODS: From March to December 2021, based on the original 3D-fast imaging employing steadystate acquisition (FIESTA) scanning sequence, 92 patients with sacral meningeal cysts were pre-operatively evaluated by multiple dimensional reconstruction MRI. The shape of nerve root and the leakage of cyst were reconstructed according to the direction of nerve root or leakage track showed on original MRI scans. Sacral canal cysts were accurately classified as including nerve root and without nerve root, so as to accurately design the incision of skin and formulate corresponding open range of the posterior wall of the sacral canal. Under the microscope intraoperation, the shape of the nerve roots inside cysts or leakage track of the cysts without nerve roots were verified and explored. After the reinforcement and shaping operation, several reexaminations of multiple dimensional reconstruction MRI were performed to understand the deformation of the nerve root and hydrops in the operation cavity, so as to formulate a persona-lized rehabilitation plan for the patients. RESULTS: Among the 92 patients with sacral mengingeal cyst, 58 (63.0%) cysts with nerve root cyst, 29 (31.5%) cysts without nerve root cyst, and 5 (5.4%) cysts with mixed sacral canal cyst. In 58 patients with nerve root cysts, the accuracy of preoperative clinical classification on MRI image reached 96.6% (56/58) through confirmation by operating microscope. Only 2 cases of large single cyst with nerve root on the head of cyst were mistaken for without nerve root type. In 29 patients with sacral cyst without nerve root, the accuracy of preoperative image reached 100% through confirmation by operating microscope. The accuracy of judging the internal nerve root and leakage of 12 cases with recurrent sacral cyst was also 100%. Two cases of delayed postoperative hydrops were found one month after operation. After rehabilitation treatment by moxibustion and bathing, the hydrops disappeared 4-6 months after operation. CONCLUSION Multiple dimensional reconstruction MRI can precisely make clinical classification of sacral meningeal cysts before operation, guide minimally invasive neurosurgery effectively, and improve the rehabilitation effect.
Humans ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Sacrum/surgery* ; Adult ; Middle Aged ; Imaging, Three-Dimensional/methods* ; Cysts/rehabilitation* ; Aged ; Adolescent ; Young Adult ; Spinal Nerve Roots/diagnostic imaging* ; Minimally Invasive Surgical Procedures ; Neurosurgical Procedures/methods*

OBJECTIVE: To investigate the effect of Tongmai Hypoglycemic Capsule (THC) on myocardium injury in diabetic cardiomyopathy (DCM) rats. METHODS: A total of 24 Sprague Dawley rats were fed for 4 weeks with high-fat and high-sugar food and then injected with streptozotocin intraperitoneally for the establishment of the DCM model. In addition, 6 rats with normal diets were used as the control group. After modeling, 24 DCM rats were randomly divided into the model, L-THC, M-THC, and H-THC groups by computer generated random numbers, and 0, 0.16, 0.32, 0.64 g/kg of THC were adopted respectively by gavage, with 6 rats in each group. After 12 weeks of THC administration, echocardiography, histopathological staining, biochemical analysis, and Western blot were used to detect the changes in myocardial structure, oxidative stress (OS), biochemical indexes, protein expressions of myocardial fibrosis, and nuclear factor erythroid 2-related faactor 2 (Nrf2) element, respectively. RESULTS: Treatment with THC significantly decreased cardiac markers such as creatine kinase, lactate dehydrogenase, and creatine kinase-MB, etc., (P<0.01); enhanced cardiac function indicators including heart rate, ejection fraction, cardiac output, interventricular septal thickness at diastole, and others (P<0.05 or P<0.01); decreased levels of biochemical indicators such as fasting blood glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol, aspartate transaminase, (P<0.05 or P<0.01); and decreased the levels of myocardial fibrosis markers α-smooth muscle actin (α-SMA), and collagen I (Col-1) protein (P<0.01), improved myocardial morphology and the status of myocardial interstitial fibrosis. THC significantly reduced malondialdehyde levels in model rats (P<0.01), increased levels of catalase, superoxide dismutase, and glutathione (P<0.01), and significantly increased the expression of Nrf2, NAD(P)H:quinone oxidoreductase 1, heme oxygenase-1, and superoxide dismutase 2 proteins in the left ventricle of rats (P<0.01). CONCLUSION THC activates the Nrf2 signaling pathway and plays a protective role in reducing OS injury and cardiac fibrosis in DCM rats.
Animals ; Diabetic Cardiomyopathies/physiopathology* ; Oxidative Stress/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Rats, Sprague-Dawley ; Myocardium/metabolism* ; Fibrosis ; Male ; Capsules ; Hypoglycemic Agents/therapeutic use* ; NF-E2-Related Factor 2/metabolism* ; Rats ; Diabetes Mellitus, Experimental/drug therapy*