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 To explore the risk factors of progression to dementia within 2 years in patients with recent subcortical small infarction (RSSI) complicated with cognitive dysfunction. Methods A total of 340 patients with RSSI complicated with cognitive dysfunction who were treated in the hospital and completed 2-year follow-up were selected from February 2021 to February 2025. According to whether the patients progressed to dementia, they were classified into dementia group (n=105) and non-dementia group (n=235). The clinical data were compared between both groups, and the independent risk factors were screened by Logistic regression analysis. Results Multivariate logistic regression analysis suggested that history of hypertension (OR=1.919), history of diabetes mellitus (OR=1.597), multiple infarctions (OR=1.455), severe white matter lesions (OR=1.595), no cognitive function training (OR=1.923), increased infarct size (OR=1.069), reduced MMSE score (OR=0.945) and increased levels of NfL (OR=1.049) and IL-6 (OR=1.038) were independent risk factors for the progression to dementia (all P<0.05). Conclusion The progression to dementia in patients with recent subcortical small infarction and cognitive dysfunction is affected by multiple factors. In clinical practice, the integration of vascular risk factors, imaging features, cognitive assessment and serum biomarkers (NfL, IL-6) helps to construct an early risk prediction model and implement targeted interventions for high-risk groups.

Objective To construct programmed cell death protein-ligand 1(PD-LI)^hi tolerogenic dendritic cell (tol-DC) derived from bone marrow of DA rats and identify its immunological function. Methods DA rat bone marrow cells were extracted, combined with recombinant mouse granulocyte macrophage colony-stimulating factor and recombinant mouse interleukin (IL)-4, and cultured for 6 days in vitro to induce the differentiation of bone marrow cells into immature dendritic cells (imDC). Lipopolysaccharide was used to stimulate cell maturation and cultured for 2 days to collect mature dendritic cells (mDC). PD-L1 lentiviral vector virus stock solution or equivalent dose lentiviral stock solution was added, and PD-L1^hitol-DC and Lv-imDC were collected after culture for 2 days. The morphology of PD-L1^hitol-DC was observed by inverted phase contrast microscope and transmission electron microscope. Real-time fluorescence quantitative reverse transcription polymerase chain reaction, Western blotting and flow cytometry were used to detect the expression level of specific markers on cell surface. CD8⁺T cells derived from Lewis rat spleen were co-cultured with imDC, mDC, Lv-imDC and PD-L1^hitol-DC, respectively. The levels of inflammatory factors in the supernatant of each group were detected by enzyme-linked immunosorbent assay. The apoptosis of T cells and the differentiation of regulatory T cells (Treg) in each group were analyzed by flow cytometry. Results The morphology of PD-L1^hitol-DC modified by PD-L1 gene was consistent with tol-DC characteristics, and the expression levels of CD80, CD86 and major histocompatibility complex (MHC) on the surface were low. After mixed culture with CD8⁺ T cells, the levels of IL-10 and transforming growth factor (TGF) -β1 in the supernatant of PD-L1^hitol-DC group were higher, the levels of tumor necrosis factor (TNF) -α and IL-17A were lower, and the apoptosis of T cells and Treg differentiation were increased. Conclusions Overexpression of PD-L1 through lentiviral vectors may successfully induce the construction of bone-marrow derived PD-L1^hitol-DC in DA rats, promote the secretion of anti-inflammatory factors and T cell apoptosis, induce the differentiation of Treg, and inhibit the immune response of allogeneic CD8⁺T cells, which provides experimental basis for the next organ transplantation immune tolerance study.

To understand the progress of, summarize the lessons learned from and analyze the challenges in the national schistosomiasis elimination program of China in 2024, this article presented the endemic situation of schistosomiasis and national schistosomiasis surveillance results in the People’s Republic of China in 2024. By the end of 2024, Shanghai Municipality, Zhejiang Province, Fujian Province, Guangdong Province and Guangxi Zhuang Autonomous Region continued to consolidate schistosomiasis elimination achievements, and 7 provinces of Jiangsu, Sichuan, Yunnan, Hubei, Hunan, Anhui and Jiangxi maintained the criteria of schistosomiasis transmission interruption. A total of 450 counties (cites, districts) were found to be endemic for schistosomiasis in China in 2024, including 26 061 endemic villages covering 73 630 500 residents at risk of infections. Among the 450 counties (cities, districts) endemic for schistosomiasis, 388 (86.22%) achieved the criteria of schistosomiasis elimination and 62 (13.78%) achieved the criteria of transmission interruption. In 2024, a total of 4 102 624 individuals received immunological tests for schistosomiasis in China, with 44 823 sero-positives identified (1.09% seroprevalence), and a total of 169 722 individuals received parasitological examinations, with 1 egg-positives detected. A total of 27 321 cases with advanced schistosomiasis were documented in China by the end of 2024. In 2024, a total of 575 686 bovines were raised in schistosomiasis-endemic villages of China, and 113 842 bovines received immunological tests, with 235 sero-positives detected (0.21% seroprevalence), while no egg-positives were identified among the 167 475 bovines receiving parasitological examinations. In 2024, snail survey was performed covering an area of 680 498.27 hm² in China, and 190 778.66 hm² snail habitats were identified, including 59.09 hm² emerging snail habitats and 704.23 hm² reemerging snail habitats. In 2024, a total of 19 665 schistosomiasis patients receiving chemotherapy with praziquantel in China, and expanded chemotherapy was given to humans at 571 722 person-times and to bovines at 306 740 herd-times. In addition, snail control with chemical treatment covered 117 111.37 hm² snail habitats across China in 2024, and the actual area of chemical treatment was 66 562.95 hm², while environmental improvements were performed in snail habitats covering an area of 1 374.26 hm². The national schistosomiasis surveillance results showed that the mean prevalence rates of Schistosoma japonicum infections were both 0 among humans and bovines in China in 2024, and no S. japonicum infection was detected in snails. These data demonstrated that the prevalence of schistosomiasis remained at a low level in China in 2024; however, the areas of snail habitats remained high and the number of fenced cattle showed a slight increase. To address these risks, it is imperative to maintain the integrated strategy with an emphasis on management of the source of S. japonicum infection and intensified snail control in high-risk areas, and to reinforce schistosomiasis surveillance and forecast and snail control in high-risk areas.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*