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.

Methods: A total of 45 patients diagnosed with de novo thoracic or lumbar SEA who underwent posterior-only surgical decompression between 2005 and 2014, with a minimum postoperative follow-up of 2 years, were included. Patients were stratified based on the presence or absence of postoperative residual ND, and neurological function was assessed immediately after surgery and at the final followup using the Frankel grading system. SITE scores, along with clinical and radiological data associated with residual ND, were collected. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to identify significant predictors. Results: Patients with residual ND had significantly lower SITE scores than those without residual ND (4.3±1.3 vs. 7±1.8, p<0.0001). Multivariate analysis identified the SITE score as an independent predictor (odds ratio, 2.70; p=0.012). ROC analysis showed that a SITE score ≤6 predicted residual ND with 73.3% sensitivity and 100% specificity, with an area under the curve of 0.877 (p<0.001). Other significant predictors included cauda equina syndrome and a shorter symptom-to-surgery interval, both of which were associated with a higher risk of residual ND. Conclusions The SITE score is a reliable and independent predictor of residual ND after surgery for SEA. SITE scores <6 indicate a significantly higher risk of postoperative ND.

Objective: To compare the effects of eccentric cycling (ECC), concentric cycling (CON), and standard care (CTL) on cardiopulmonary capacity, respiratory health, and quality of life (QoL) in patients with chronic kidney disease (CKD). Methods: Thirty-one CKD patients were divided into the CTL, CON, and ECC groups. The CON and ECC groups participated in 8-week, 24-session cycling programs. Outcomes were assessed through cardiopulmonary exercise tests, respiratory function tests, and the 36-Item Short Form Survey Instrument questionnaire. Results: The ECC group achieved significant improvements in maximal oxygen uptake, while the CTL group showed a decline. For oxygen uptake efficiency slope, significant changes were observed only in the ECC group, with a group-by-time interaction effect compared to CTL. Furthermore, the ECC group demonstrated the most significant increase in diaphragm movement and a significant increase in diaphragm thickness, with comparisons indicating that ECC outperformed both CTL and CON. Regarding QoL, the ECC group exhibited significantly greater improvements in Physical Component Summary and Mental Component Summary, with statistically significant differences compared with the CTL and CON groups. Conclusion ECC is a low-effort, high-benefit exercise modality that significantly enhances cardiopulmonary fitness, respiratory function, and QoL in patients with CKD.

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.

Objective Depression,a most common psychiatric disease,is defined in Traditional Chinese Medicine(TCM)as Yu Syndrome,i.e.,depression disorder,or Baihe Disease,i.e.,lily bulb disease,a category of emotional disorders treated with lily-based TCM preparations.In TCM,depression is managed through syndrome differentiation and treatment,which is characterized by high efficacy and safety.However,there is no unified standard for the classification of depression syndromes,which leads to a disconnection between the analysis of patients'medication patterns and their actual syndromes and hinders the study of medication patterns specific to particular syndromes.Therefore,this study is focused on investigating the medication patterns of different sub-types of depression patients based on an objective classification system of depression.Methods We searched for and retrieved clinical literature on TCM formulas for depression from relevant databases,including China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Database,Sinomed,Web of Science,and PubMed.Information on patient symptoms and medication was standardized.Then,the symptoms and the medication frequency of depression patients were statistically analyzed.We used the K-means clustering method combined with implicit structural analysis to objectively categorize depression patients into sub-types.In addition,the main symptoms and core TCM formulas of each sub-type of depression patients were identified.On the basis of objective classification system,we also statistically analyzed the characteristics of herbs used on depression patients,including the 4 basic properties,the 5 flavors,the attributes,the therapeutic efficacy,and the co-occurrence patterns,which may help reveal the medication patterns.Results A total of 3 537 publications and 4 434 prescriptions were included in the analysis.By using the K-means algorithm and latent structure analysis methods,patients with depression were categorized into 9 sub-types,with Cluster 6 accounting for the largest proportion.The most common symptoms among depression patients were insomnia and a depressed mood.Medication frequency analysis showed that Radix Bupleuri(Chai Hu),Radix Paeoniae Alba(Bai Shao),Poria(Fu Ling),Rhizoma Chuanxiong(Chuan Xiong),and Radix Curcumae(Yu Jin)were the most commonly used TCM herbs.For the depression sub-types of Clusters 1,2,and 6,blood-activating and stasis-dissolving herbs were used most often.The depression sub-types of Clusters 3,4,5,8,and 9 were mainly treated with qi-regulating herbs,while the depression sub-type of Cluster 7 was treated with qi-supplementing herbs.Depression patients were mostly treated with herbs that were cold or warm in nature and had sweet,bitter,and pungent flavors.Moreover,treatments for Cluster 1 and Cluster 6 mainly targeted the spleen meridian,while those for Cluster 2,Cluster 3,Cluster 4 and Cluster 5 mainly targeted the heart meridian.The treatments for the other sub-types mainly targeted the liver meridian.The core TCM formulas for the 9 depression sub-types included Zishui Qinggan Decoction,Danzhi Xiaoyao Powder,Huanglian Wendan Tang,Chaihu Guizhi Tang,Modified Xiaoyao Powder,Qinggan Jieyu Tang,Xiaoyao Powder,Xuefu Zhuyu Decoction,and Bazhen Decoction.The most commonly used Chinese herbal medicinal formulas were Gan Cao-Chai Hu,Bai Shao-Chai Hu,and Chen Pi-Chai Hu.Conclusion Based on machine learning,this study reveals the scientific aspects of TCM typing and syndrome-based treatment.It clarifies the rationale for targeting different symptoms in depression treatment and provides theoretical support for clinicians to make medication prescriptions.It also presents a new perspective for investigating TCM medication patterns.

Objective To investigate the effect of programmed death-1(PD-1)on cell infiltration in muscle tissue and immune response types in mice infected with Trichinella spiralis.Methods C57BL/6J wild-type(WT)and PD-1 deficient(PD-1-/-)mice were infected with T.spiralis(400 muscle larvae per mouse),and samples were collected on day 35 after infection.The proportions of infiltrating inflammatory cells and fibroblasts around encapsulated larvae were assessed by immunohistochemistry.The expression levels of interferon-γ(IFN-γ),interleukin(IL)-4,IL-5,IL-13,and eotaxin in muscle tissue were measured using enzyme-linked immunosorbent assay.Peripheral blood and spleen were collected at different time points after infection.The percentages of CD4+IFN-γ+Th1 and CD4+IL-4+Th2 within CD4+T cells population in peripheral blood and spleen of mice were analyzed using flow cytometry.Results The proportions of eosinophils and fibroblasts among total infiltrating cells around the encapsulated larvae in the muscle of PD-1-/-mice were significantly lower than those in WT mice after T.spiralis infection(P＜0.01).The infected PD-1-/-mice exhibited higher proportions of macrophages,T cells and B cells in total infiltrating cells than the infected WT mice(P＜0.01).The levels of IL-4,IL-5,IL-13,and eotaxin in the muscle tissue of infected PD-1-/-mice were significantly lower than those in infected WT mice(P＜0.05).However,IFN-γ levels were not significantly different between the infected WT and PD-1-/-mice.The proportions of Th2 cells in CD4+T cells from peripheral blood and spleen of infected PD-1-/-mice were significantly lower than those in infected WT mice,whereas the proportion of Th1 cells showed no difference among the infected groups.Conclusions PD-1 deletion results in decreased expression of key chemokines of eosinophils and key cytokines of fibroblast formation,and a corresponding decrease in inflammatory cells in muscle in T.spiralis-infected mice.This effect may be associated with a diminished Th2 immune response caused by PD-1 deletion.

Objective To analyze the impact of sarcopenia on the efficacy and adverse reactions of immunotherapy combined with chemotherapy in patients with advanced gastric cancer.Methods Patients with locally advanced or metastatic gastric cancer confirmed by pathology who were not eligible for radical surgery were selected as study subjects.A body composition analyzer was used to measure the appendicular muscle mass of the patients and calculate the skeletal muscle mass index(SMI).Based on the SMI,the patients were divided into sarcopenia group and non-sarcopenia group.On the basis of nutritional intervention and comprehensive exercise therapy,the patients were administered immu-notherapy combined with chemotherapy.The efficacy and adverse reactions were evaluated.The primary endpoint was progression-free survival(PFS),and the secondary endpoints were the objec-tive response rate(ORR)and treatment-related adverse reactions.Results A total of 52 gastric cancer patients were included,with 23 in the sarcopenia group and 29 in the non-sarcopenia group.The median PFS in the non-sarcopenia group was 9.8 months(95％CI,8.9 to 12.4),and was 5.4 months in the sarcopenia group(95％CI,4.9 to 8.1).The median PFS in the non-sarcopenia group was longer than that in the sarcopenia group,and the difference was statistically significant[HR(95％CI)=0.41(0.23 to 0.73),P=0.003].The results of the multivariate Cox propor-tional hazards regression model showed that comorbidities,treatment cycles,and sarcopenia were all independent prognostic factors affecting the PFS of gastric cancer patients(P＜0.05).The ORR in the non-sarcopenia group was 48.28％(14/29),and was 17.39％(4/23)in the sarcopenia group(x2=5.276,P＜0.05).Treatment-related adverse reactions with grading ≥3 in both groups were mainly hematological toxicities.In the non-sarcopenia group,the incidence of grading ≥ 3 treat-ment-related adverse reactions was 27.59％(8/29),and the incidence of grading＜3 treatment-re-lated adverse reactions(including those with no adverse reactions)was 72.41％(21/29).In the sarcopenia group,the incidence of grading ≥3 treatment-related adverse reactions was 56.52％(13/23),and the incidence of grading＜3 treatment-related adverse reactions(including those without adverse reactions)was 43.48％(10/23).The incidence of grading ≥3 treatment-related adverse reactions in the non-sarcopenia group was lower than that in the sarcopenia group(P=0.035).Conclusion For patients with locally advanced or metastatic gastric cancer complicated with sarcopenia,the median PFS of immunotherapy combined with chemotherapy is shorter,the ORR is lower,and the incidence of treatment-related adverse reactions is increased.Therefore,ear-ly intervention for sarcopenia should be implemented to improve the quality of life of patients with advanced gastric cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.