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.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

AIM:This study aims to investigate the molecular mechanism by which tubeimoside I(TBMS1)inhibits Snail expression in pancreatic cancer cells(PANC-1).METHODS:Human pancreatic cancer PANC-1 cells were cultured in vitro.The inhibitory effect of TBMS1 on PANC-1 cells was assessed using the MTT assay,and the data were analyzed based on the IC50 value of TBMS1.The impact of TBMS1 on the clonal formation ability of PANC-1 cells was evaluated through colony formation assays.The Transwell assay was employed to assess the effect of TBMS1 on the migrato-ry capability of PANC-1 cells.Apoptosis and cell cycle alterations in PANC-1 cells were analyzed using acridine orange staining and flow cytometry.The expression of Snail protein in pancreatic cancer and its relationship with survival of the patients were analyzed using the GEPIA database and Kaplan-Meier Plotter data.Immunofluorescence staining was con-ducted to investigate the effect of TBMS1 on Snail expression,while Western blot was used to evaluate the expression of poly(ADP-ribose)polymerase(PARP),E-cadherin and Snail in the cells.The ubiquitination of Snail protein was mea-sured using immunoprecipitation techniques.RESULTS:As the concentration of TBMS1 increased,the survival rate and number of clones formed by PANC-1 cells progressively decreased,leading to apoptosis,cleavage of PARP,and cell cycle arrest in the G1 phase.There was also a reduction in the proportion of cells in the S phase and a decrease in cell migration ability.The expression of Snail protein,a critical factor in cell migration,was inhibited,while E-cadherin protein levels were increased.Treatment with the proteasome inhibitor MG132 was able to reverse the suppression of Snail protein ex-pression caused by TBMS1.Immunoprecipitation results indicated that TBMS1 enhances the ubiquitination and subse-quent degradation of Snail protein.CONCLUSION:TBMS1 effectively inhibits the malignant progression of pancreatic cancer cells by promoting the ubiquitination and degradation of Snail protein in PANC-1 cells.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

objective:To investigate the factors influencing failure of preoperative optimisation with exclusive enteral nutrition(EEN)in patients with Crohn's disease(CD)and to construct a predictive model.Methods:Clinical data of 161 patients with Crohn's disease who underwent surgical treatment after preoperative EEN optimization at the Inflammatory Bowel Disease Center of General Surgery of the Eastern Theater General Hospital from June 2021 to November 2023 were retrospectively analyzed.Patients were divided into two groups according to the preoperative EEN optimisation effect:optimisation success and optimisation failure.Logistic regression analysis was used to determine the factors influencing optimisation failure,and a column line graphical model was constructed using R4.4.0 software.Results:After at least 4 weeks of EEN treatment,a total of 44 patients(27.33%)failed preoperative optimization.Multifactorial analysis showed that FC>500 μg/g and use of corticosteroids were independent risk factors for failure of preoperative optimization,and an increase in prealbumin after 1 week of treatment was a protective factor for successful preoperative optimization.Based on the results of the multivariate analysis,a column graph was constructed,and the C-index was 0.873(95%CI=0.783～0.964).The calibration curve shows that the probability of failure predicted by the nomogram model prior to optimization is in good agreement with the actual probability.The clinical decision curve confirms that the risk threshold is between 0.03～0.96,and the nomogram model can be used to predict the failure of preoperative EEN optimization over other treatment options.Conclusion:FC>500μg/g and history of corticosteroid use were independent risk factors for failure of preoperative optimization,and an increase in prealbumin after 1 week of treatment was a protective factor for successful preoperative optimization.The nomogram constructed on the basis of these results has a good predictive value and is clinically applicable.

Objective:To analyze the genetic characteristics and evolutionary origin of hepatitis B virus (HBV) subgenotype D3 in China in 2020.Methods:Serum samples and demographic details from patients infected with HBV D3 subgenotype were collected. HBV genomic sequences were obtained by nested PCR amplification and subsequent sequencing. Phylogenetic analysis, nucleotide homology, amino acid mutation and evolution rate of the S protein were conducted by comparing with reference sequence using bioinformatics tools.Results:The complete HBV gene sequences of 14 samples of D3 subtype HBV were obtained. Compared with 97 reference sequences, it was found that the sequences with the highest homology were from India, Mongolia, Iran and China, with the homology ranging from 96.0% to 97.9%. Mutations of 24 amino acids were found in 14 strains of D3 subtype. Among them, T131A, Y134F and T140I were associated with immune escape-related mutations. The genetic diversity of HBV D3 subtype increased slowly before 1975, remained relatively constant from 1975 to 2000, and began to decline after 2000. Evolutionary rate analysis showed that samples QGLD D3-02 and 03 originated from a common ancestor with the Iranian reference strain in 1872, and the other 12 samples QGLD D3-04-17 originated from a common ancestor with the Mongolian reference strain in 1843.Conclusions:The gene sequence of HBV D3 subtype in China had the highest homology with reference sequences from India, Iran, Mongolia and China. Evolutionary rate analysis revealed that 14 cases of HBV D3 subtype originated from a common ancestor with reference strains from Mongolia and Iran, which enriched the sequence and evolution information of HBV D3 subtype and provided a reference basis for the molecular epidemiological study of HBV.

Objective:To identify risk factors influencing clinical efficacy of endoscopic resection of small rectal neuroendocrine tumor (NETs).Methods:A retrospective analysis was conducted on patients with rectal NETs ≤10 mm who underwent endoscopic resection from 2013 to 2022. Patients were divided into the endoscopic submucosal dissection (ESD) group and the endoscopic mucosal resection with ligation (EMRL) group according to the treatment methods. After comparing the baseline data, propensity score matching was performed to compare the rates of R1 resection and adequacy of vertical margin distance.Results:A total of 186 patients were included in this study, with 139 receiving ESD and 47 receiving EMRL. The R1 resection rates were 12.2% (17/139) and 2.1% (1/47) in the ESD and EMRL group, respectively ( χ2=3.027, P=0.082). A significant difference in vertical margin adequacy was observed between the two groups [69.1% (96/139) VS 85.1% (40/47), χ2=4.598, P=0.032]. After propensity score matching, 46 pairs of cases were included, and there were no significant differences in the R1 resection rate [6.5% (3/46) VS 2.2% (1/46), χ2=0.261, P=0.609] and vertical margin adequacy [78.3% (36/46) VS 84.8% (39/46), χ2=0.649, P=0.420] between the two groups. Univariate and multivariate Logistic regression analyses revealed that operator experience and preoperative biopsy were independent risk factors for inadequate margin. Conclusion:Treatment method may not be the key factor affecting the distance of the vertical margin after endoscopic resection, but preoperative biopsy and operator experience have a significant impact on margins. Biopsy before endoscopic resection should be avoided, and less experienced doctors are recommended to use EMRL method for small NETs due to its ease of execution.

In February 2025, a local cluster outbreak caused by the D8 genotype Measles virus (MV) was first discovered in Henan Province. Epidemiological investigations and laboratory testing were conducted, including the collection of serum and throat swabs for MV IgM antibody and nucleic acid detection, virus isolation and genetic homology analysis. Measures such as close contact tracing, vaccination rate assessment and supplementary immunization activities were implemented, successfully preventing broader community transmission. A total of three cases were reported during the outbreak, including one imported-related adolescent and two secondary local adult cases. All cases presented with typical symptoms such as fever and rash. Both adult cases were complicated by pneumonia, with one case developing into severe pneumonia. MV genotyping showed that the two secondary cases were both the D8 genotype, with the viral sequences being completely homologous to the Kazakhstan strain. Among the close contacts, 98.2% were adults, and 142 individuals received emergency vaccination.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.