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.

Background With China's socio-economic development, the dietary structure of Chinese residents has gradually shifted from a traditional Eastern pattern characterized by high carbohydrate intake to a relatively high-fat Western dietary model, alongside a growing burden of chronic diseases. However, dietary changes may vary across different occupational groups. Objective To analyze the long-term trends in dietary energy and three major macronutrient intake among various occupational groups aged 18-59 years in nine provinces of China from 1989 to 2018, providing a scientific basis for developing occupation-specific dietary intervention strategies. Methods Based on 11 waves of data (1989–2018) from the China Health and Nutrition Survey (CHNS), 57458 employed individuals aged 18-59 years were selected as observation subjects after applying inclusion and exclusion criteria. Joinpoint regression was used to analyze temporal trends by calculating the annual percentage change (APC) and average annual percentage change (AAPC) with 95% confidence intervals. The analysis covered the energy supply ratio from the three major macronutrients and the proportion of the participants within the acceptable macronutrient distribution ranges (AMDR) for the total occupational population and specific occupational group. Results From 1989 to 2018, the dietary structure of the occupational population in the nine provinces gradually Westernized. The carbohydrate energy supply ratio showed a decreasing trend (AAPC=−0.77%, 95%CI: −1.00%, −0.55%, P<0.05), while the fat energy supply ratio increased (AAPC=1.42%, 95%CI: 1.07%, 1.77%, P<0.05). The protein energy supply ratio remained largely unchanged (AAPC=−0.19%, 95%CI: −0.08%, 0.46%, P>0.05), and the total energy intake declined (AAPC=−0.62%, 95%CI: −0.94%, −0.30%, P<0.05). Among the occupational groups studied, white-collar workers were the first to show characteristics of dietary Westernization. Using the AMDR as the criterion, their average fat energy supply ratio first exceeded the AMDR upper limit (30%) in 1993, and their average carbohydrate energy supply ratio first fell below the AMDR lower limit (50%) in 2006, both time points being earlier than other occupational groups. Agricultural workers lagged in this transition (their average fat energy supply ratio first reached ≥30% in 2011), but their rate of increase in the fat energy supply ratio was the fastest among all occupational groups (AAPC=1.59%, 95%CI: 0.90%, 2.30%, P<0.05; P for trend comparison between groups<0.05). The other three occupational groups (blue-collar workers, service workers, and others) generally fell between these two extremes. Taking the fat energy supply ratio as an example, their AAPCs of increase were 1.02% (95%CI: 0.73%, 1.32%, P<0.05) for blue-collar workers, 0.85% (95%CI: 0.61%, 1.09%, P<0.05) for service workers, and 0.88% (95%CI: 0.58%, 1.17%, P<0.05) for others. Conclusion From 1989 to 2018, the macronutrient structure of the occupational population aged 18-59 years in nine China provinces is gradually Westernized. The energy supply ratios of carbohydrates and proteins primarily face issues of potential inadequacy, while the fat energy supply ratio is excessively high. Furthermore, the patterns of change vary among different occupational groups. It is recommended to propose personalized dietary advice tailored to specific occupations.

Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.

Background The global prevalence of obesity is on the rise and is closely associated with various chronic non-communicable diseases such as cardiovascular diseases and diabetes. There is a relative lack of long-term dynamic studies on compound obesity among occupational populations. Objective To explore the changing trends of compound obesity among different occupational groups aged 18–59 years in nine provinces (autonomous regions, municipalities) of China from 1993 to 2018, and to provide a scientific basis for formulating targeted weight management strategies for occupational populations. Methods A total of 34519 employed adults aged 18–59 years from nine waves of the China Health and Nutrition Survey in1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, and 2018 were included. Obesity types were determined according to the Chinese Adult Weight Management Guidelines. The Joinpoint regression model was used to calculate the average annual percentage change (AAPC) and annual percentage change (APC) of compound obesity rates. Results From 1993 to 2018, the age-standardized compound obesity rate among the occupational population aged 18–59 years showed an upward trend (AAPC=6.24%, 95%CI: 5.09%, 7.41%, P<0.05). The AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, other occupational groups, service workers, and white-collar workers were 9.55% (7.51%, 11.64%), 6.18% (4.70%, 7.69%), 5.53% (2.27%, 8.90%), 4.07% (2.40%, 5.77%), and 3.89% (2.16%, 5.65%), respectively, with statistically significant differences (P<0.05). Among males, the AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, other occupational groups, service workers, and white-collar workers were 11.52% (7.71%, 15.47%), 7.28% (6.18%, 8.40%), 5.92% (2.28%, 9.69%), 5.56% (2.11%, 9.12%), and 4.22% (2.33%, 6.15%), respectively, with statistically significant differences (P<0.05). Among females, the AAPCs (95%CI) for age-standardized compound obesity rates among agricultural workers, blue-collar workers, and white-collar workers were 7.45% (6.22%, 8.69%), 3.72% (0.84%, 6.67%), and 2.41% (0.17%, 4.70%), respectively, with statistically significant differences (P<0.05). A turning point was observed in the compound obesity rate among female white-collar workers in 2006, with an APC (95%CI) of 9.69% (5.24%, 14.33%) from 2006 to 2008, showing a statistically significant difference (P<0.05), while no significant trend was found from 1993 to 2006 (P>0.05). No significant trends were observed in the age-standardized compound obesity rates among females in other occupational groups and service workers (P>0.05). Conclusion The compound obesity rate among China's occupational population is increasing rapidly, with the growth rate among agricultural workers being particularly prominent. In 2018, male white-collar workers and female agricultural workers exhibit relatively high compound obesity rates, warranting focused attention. Although the overall growth rate among service workers is slower, the expanding workforce in this sector indicates that obesity remains a serious issue. Targeted occupational health and weight management interventions are needed in the future.

Background With rising obesity rates and earlier hypertension onset among occupational populations, there is an urgent need to elucidate the long-term cardiovascular impacts of dynamic body weight patterns. Current evidence lacks trajectory modeling studies examining occupation-specific prevention strategies. Objective To investigate the association between long-term body mass index (BMI) trajectories and incident hypertension risk in Chinese working adults, and to examine occupation-specific heterogeneity in this relationship. Methods A dynamic sub-cohort of 4 413 occupational participants was constructed from ten survey waves (1991–2018) of the China Health and Nutrition Survey (CHNS). Eligible individuals had valid key BMI records at three or more independent follow-ups before the outcome event; the individual baseline was set as the year of their first participation in the survey. Group-based trajectory modeling (GBTM) was used to identify BMI change patterns. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence interval (CI) for hypertension incidence across trajectory groups, with stratified analysis by occupational categories. Results Among 4413 occupational participants, 1441 incident hypertension cases were reported during the follow-up window. Three long-term BMI trajectories were identified: low-stable, moderate-slowly rising, and high-rising. In Model 3 with all selected confounders adjusted, compared with the low-stable group, the moderate-slowly rising group showed a significantly higher risk of incident hypertension (HR=1.28, 95%CI: 1.12, 1.45; P < 0.001), as did the high-increasing group (HR=1.80, 95%CI: 1.45, 2.23; P < 0.001). The stratified analyses revealed occupational differences in the association. Among agricultural workers, the moderate-slowly rising group (HR=1.35, 95%CI: 1.15, 1.57) and the high-rising group (HR=2.00, 95%CI: 1.50, 2.66) both exhibited significantly increased risks of hypertension (P<0.001). Conversely, among white-collar workers, an increased risk of hypertension was observed only in the high-rising group (HR=1.97, 95%CI: 1.23, 3.17; P<0.05). Conclusion Rapid BMI escalation constitutes a significant risk factor for incident hypertension in occupational populations. Targeted long-term weight monitoring and tailored interventions are recommended for high-risk occupations.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

Objective To systematically evaluate the similarities between six-gene-modified pig red blood cells and human red blood cells in multiple dimensions, including cell morphology, hematological parameters, microbial safety and serological compatibility, and to verify the feasibility of using six-gene-modified pig red blood cells as an alternative for blood transfusion in cases of traumatic hemorrhage. Methods Blood samples from O-type human donor, six-gene-modified O-type pig and wild-type O-type pig were collected. The pig gene-modified phenotype was identified by flow cytometry. The morphology was observed using field emission scanning electron microscopy and the diameter was measured. Hematological tests, including blood routine, osmotic fragility test, adenosine triphosphate (ATP) and 2,3-diphosphoglycerate (2,3-DPG) content and partial pressure of oxygen at 50% hemoglobin saturation (P50), were conducted to assess the deformability of red blood cells. Real-time fluorescent quantitative polymerase chain reaction was used to screen for zoonotic viruses, and cross-matching was performed using column gel method, condensation amine method and saline tube method. Results The six-gene-modified pig red blood cells successfully knocked out the α-Gal, Neu5Gc and Sda antigens and expressed hCD55. Wild-type pig red blood cells expressed these three antigens but did not express human complement regulatory proteins. All three types of red blood cells presented a biconcave discoid morphology. The diameter of O-type human red blood cells was larger than that of the six-gene-modified porcine red blood cells and wild-type pig red blood cells (all P < 0.05). The average red blood cell volume and average red blood cell hemoglobin of O-type humans were higher than those of the six-gene-modified pig red blood cells, and the distribution width-variable coefficient of O-type human red blood cells was lower than that of the six-gene-modified pig red blood cells (all P < 0.05). There were no statistically significant differences in the permeability fragility, ATP content, 2,3-DPG content, P50 and red blood cell deformability indicators of the three types of red blood cells (all P > 0.05). The screening for zoonotic viruses in human and animal cells of the six-gene-modified pigs and wild-type pigs was negative. The cross-matching results showed that in the column gel method and condensation amine method, the primary and secondary sides of the six-gene-modified pig red blood cells did not agglutinate with human red blood cells, while the wild-type pig red blood cells agglutinated with human red blood cells in all cross-matching methods. Conclusions The six-gene-modified pig red blood cells are highly similar to human red blood cells in terms of morphology, key hematological parameters, energy metabolism, oxygen-carrying capacity, mechanical properties, microbial safety and serological compatibility, providing an experimental basis for the clinical transformation of xenotransfusion.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.