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.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

Objective: To investigate the level of cancer prevention and control literacy and its influencing factors among residents of the Xinjiang Production and Construction Corps (hereafter referred to as "the Corps"), so as to provide evidence for developing cancer health education strategies. Methods: rom July to October 2024, permanent residents aged 15 to 69 years in the Corps were selected as research subjects by multi-stage random sampling method. Data on basic characteristics, smoking status, self-rated health status, and cancer prevention and control literacy were collected through the National Resident Cancer Prevention and Control Literacy Questionnaire. Multivariable logistic regression model was used to analyze the influencing factors for the level of cancer prevention and control literacy among residents. Results: A total of 5 398 residents were surveyed, including 2 166 males (40.13%) and 3 232 females (59.87%). The mean age of the participants was (43.80±0.17) years. A total of 1 942 individuals were identified as having cancer prevention and control literacy, with an overall literacy rate of 35.98%. Multivariable logistic regression analysis revealed that age (35-<55 years, OR=1.245, 95%CI: 1.068-1.450; 55-69 years, OR=1.232, 95%CI: 1.022-1.486), ethnicity (ethnic minorities, OR=0.705, 95%CI: 0.599-0.829), education level (junior high school, OR=1.779, 95%CI: 1.429-2.214; senior high school, OR=1.929, 95%CI: 1.533-2.429; college and above, OR=2.364, 95%CI: 1.891-2.955), household registration (urban, OR=1.254, 95%CI: 1.085-1.450), family history of cancer (unknown, OR=0.704, 95%CI: 0.533-0.931), smoking status (current smoker, OR=0.822, 95%CI: 0.702-0.962), and self-rated health (fair/poor, OR=1.179, 95%CI: 1.048-1.327) were associated with cancer prevention and control literacy. Conclusions The overall cancer prevention and control literacy rate among residents was 35.98%. It is recommended that priority be given to enhancing cancer prevention and control literacy among residents with the following characteristics: aged 15-<35 years, ethnic minority, education below junior high school level, rural residence, current smoking, and self-rated good health status.

Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

Background/Aims: This study assessed the long-term efficacy and safety of tenofovir alafenamide (TAF) in real-world settings. Methods: Patients who were candidates for TAF treatment and were followed up at 12-week intervals over 192 weeks were enrolled in this study. Results: One hundred and forty-four patients (50 treatment-naive and 94 treatment-experienced) were included in this study. The cumulative incidence rates of cirrhosis and hepatocellular carcinoma at 192 weeks were 3.9% and 0.7%, respectively. In treatment-naive patients, the rates of a virological response, HBeAg conversion, and HBsAg loss at 192 weeks were 100%, 33.3%, and 2%, respectively. The treatment-naive patients exhibited higher baseline HBsAg levels than the treatment-experienced patients (4.31 log10IU/mL vs. 3.97 log10IU/mL). A significant decrease in the HBsAg levels from the baseline was observed at 144 and 192 weeks in the treatment-naive patients (p=0.01). The baseline body mass index (BMI) <25 kg/m2 (p=0.02) and HBsAg <3.3 log10IU/mL (p=0.04) were identified as predictive factors for a decrease in HBsAg ≥0.5 log10IU/mL at 48 weeks. The eGFR levels were consistently lower in the treatment-experienced patients throughout the study. Although the treatment-naive patients showed no abnormal increases in urinary URBP, the treatment-experienced patients showed elevated urinary β2MG and NAG levels at the baseline, which decreased over the treatment course. The total cholesterol, triglyceride, and low-density lipoprotein levels were similar in both groups. Conclusions Prolonging the TAF treatment duration enhances the virological response rate. The decline in HBsAg levels was more significant in the treatment-naive patients than in the treatment-experienced patients. The baseline BMI <25 kg/m2 and HBsAg <3.3 log10IU/mL were predictive factors for a significant decline in HBsAg at 48 weeks. TAF has high renal safety and no significant impact on lipid levels.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background/Aims: This study assessed the long-term efficacy and safety of tenofovir alafenamide (TAF) in real-world settings. Methods: Patients who were candidates for TAF treatment and were followed up at 12-week intervals over 192 weeks were enrolled in this study. Results: One hundred and forty-four patients (50 treatment-naive and 94 treatment-experienced) were included in this study. The cumulative incidence rates of cirrhosis and hepatocellular carcinoma at 192 weeks were 3.9% and 0.7%, respectively. In treatment-naive patients, the rates of a virological response, HBeAg conversion, and HBsAg loss at 192 weeks were 100%, 33.3%, and 2%, respectively. The treatment-naive patients exhibited higher baseline HBsAg levels than the treatment-experienced patients (4.31 log10IU/mL vs. 3.97 log10IU/mL). A significant decrease in the HBsAg levels from the baseline was observed at 144 and 192 weeks in the treatment-naive patients (p=0.01). The baseline body mass index (BMI) <25 kg/m2 (p=0.02) and HBsAg <3.3 log10IU/mL (p=0.04) were identified as predictive factors for a decrease in HBsAg ≥0.5 log10IU/mL at 48 weeks. The eGFR levels were consistently lower in the treatment-experienced patients throughout the study. Although the treatment-naive patients showed no abnormal increases in urinary URBP, the treatment-experienced patients showed elevated urinary β2MG and NAG levels at the baseline, which decreased over the treatment course. The total cholesterol, triglyceride, and low-density lipoprotein levels were similar in both groups. Conclusions Prolonging the TAF treatment duration enhances the virological response rate. The decline in HBsAg levels was more significant in the treatment-naive patients than in the treatment-experienced patients. The baseline BMI <25 kg/m2 and HBsAg <3.3 log10IU/mL were predictive factors for a significant decline in HBsAg at 48 weeks. TAF has high renal safety and no significant impact on lipid levels.

Background/Aims: This study assessed the long-term efficacy and safety of tenofovir alafenamide (TAF) in real-world settings. Methods: Patients who were candidates for TAF treatment and were followed up at 12-week intervals over 192 weeks were enrolled in this study. Results: One hundred and forty-four patients (50 treatment-naive and 94 treatment-experienced) were included in this study. The cumulative incidence rates of cirrhosis and hepatocellular carcinoma at 192 weeks were 3.9% and 0.7%, respectively. In treatment-naive patients, the rates of a virological response, HBeAg conversion, and HBsAg loss at 192 weeks were 100%, 33.3%, and 2%, respectively. The treatment-naive patients exhibited higher baseline HBsAg levels than the treatment-experienced patients (4.31 log10IU/mL vs. 3.97 log10IU/mL). A significant decrease in the HBsAg levels from the baseline was observed at 144 and 192 weeks in the treatment-naive patients (p=0.01). The baseline body mass index (BMI) <25 kg/m2 (p=0.02) and HBsAg <3.3 log10IU/mL (p=0.04) were identified as predictive factors for a decrease in HBsAg ≥0.5 log10IU/mL at 48 weeks. The eGFR levels were consistently lower in the treatment-experienced patients throughout the study. Although the treatment-naive patients showed no abnormal increases in urinary URBP, the treatment-experienced patients showed elevated urinary β2MG and NAG levels at the baseline, which decreased over the treatment course. The total cholesterol, triglyceride, and low-density lipoprotein levels were similar in both groups. Conclusions Prolonging the TAF treatment duration enhances the virological response rate. The decline in HBsAg levels was more significant in the treatment-naive patients than in the treatment-experienced patients. The baseline BMI <25 kg/m2 and HBsAg <3.3 log10IU/mL were predictive factors for a significant decline in HBsAg at 48 weeks. TAF has high renal safety and no significant impact on lipid levels.