Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To discuss the factors influencing the occurrence of frailty in patients with acute myocardial infarction(AMI)after receiving interventional treatment,and to construct a prediction model,to draw a nomogram,and to make the validation of the model.Methods Using convenient sampling method,a total of 462 patients with AMI,who were admitted to the Department of Cardiovascular Medicine of three Grade Ⅲ-A hospitals in Shandong Province of China from July 2023 to January 2024,were selected as the study subjects.Among them,324 AMI patients encountered from July 2023 to November 2023 were selected as modeling group,and logistic regression was used to construct a risk prediction model and draw a nomogram to visualize the model.The remaining 138 AMI patients encountered from December 2023 to January 2024 were used as the validation group.The receiver operating characteristic(ROC)curve and Hosmer-Lemeshow testing were adopted to verify the predictive effect of the model.Results Of 324 patients in the modeling group,170(52.47％)developed frailty.Univariate analysis showed that significant differences in age,education level,body mass index(BMI),Charlson comorbidity index,grip strength,walking speed,brain natriuretic peptide precursor level,physical exercise,multiple medication,and kinesophobia existed between the two groups(all P＜0.05).Multivariate logistic regression analysis revealed that age,BMI,Charlson comorbidity index,grip strength,walking speed,NT-ProBNP precursor level,physical exercise,multiple medication,and kinesophobia were the influencing factors of frailty in patients with AMI after receiving interventional treatment,with an OR value of 1.061,0.630,1.529,0.931,0.005,0.358,1.783,2.929,and 0.497 respectively.The above nine factors were used as independent variables to draw the nomogram,the area under ROC curve of the model was 0.851(95％CI:0.809-0.892),the optimal critical value was 0.562,the sensitivity was 84.1％,and the specificity was 72.1％.Hosmer-Lemeshow goodness of fit testing showed that the model had anx2=12.957 and P=0.113.Conclusion The frailty condition of AMI patients after receiving interventional treatment is at a low to medium levels.The frailty risk prediction model constructed in this study has good prediction effect,which can provide guidance for clinical nurses to timely identify high-risk patients and to promptly adopt interventional measures.

To investigate the association between obesity-related metabolic indices and the risk of knee osteoarthritis(KOA) in middle-aged and older Chinese adults(≥45 years) using data from the China Health and Retirement Longitudinal Study(CHARLS).

Objective To observe value of real-time functional MRI neurofeedback(rtfMRI-NF)for modulating brain activity changes in obese adults.Methods Twenty-two obese adults were prospectively recruited,3-week rtfMRI-NF training intervention was conducted.Barratt impulsivityness scale version 11(BIS-11),three-factor eating questionnaire(TFEQ)and food rating scales were scored before and after intervention,and whole-brain resting state fMRI(rs-fMRI)data were obtained.Clinical scale scores,rs-fMRI regional homogeneity(ReHo)values and amplitude of low frequency fluctuation(ALFF)values were compared before and after intervention.Then brain regions with differences of ReHo and ALFF values before and after intervention were explored,and correlations of values of rs-fMRI data differences and clinical scale score differences were analyzed.Results BIS-11 score,TFEQ emotional eating(TFEQ-EE)score and food rating scales scores decreased,while TFEQ cognitive restraint(TFEQ-CR)scores increased in obese adults after intervention(all P＜0.05).ReHo values in right inferior frontal gyrus,right anterior cingulate and left precuneus increased,while of left middle temporal gyrus decreased(all corrected P＜0.05).ALFF values of left precuneus increased,whereas of left middle occipital gyrus,right superior occipital gyrus,left calcarine fissure and surrounding cortex and left supramarginal and angular gyrus decreased(all corrected P＜0.05).ReHo difference in right anterior cingulate was negatively correlated with BIS-11 scores difference(r=-0.601,P＜0.05),and ALFF difference in left precuneus was negatively correlated with TFEQ-EE difference(r=-0.478,P＜0.05).Conclusion rtfMRI-NF intervention could correct disorder of cerebral functional areas in obese adults and change high-calorie food preference and poor dietary habits.

Objective:To investigate the correlation between leucine-rich α2 glycoprotein (LRG) and endoscopic activity in patients with Crohn′s disease (CD), based on the assessment of inflammation in small intestinal lesion by double-balloon enteroscope (DBE).Methods:From 15 August 2022 to 22 August 2023, the clinical data of 139 patients with small bowel CD diagnosed by DBE at the First Affiliated Hospital of Anhui Medical University were prospectively collected, which included fecal calprotectin (FC), C-reactive protein (CRP), white blood cell count, hemoglobin, albumin, Crohn′s disease activity index (CDAI), and simple endoscopic score for Crohn′s disease (SES-CD). According to the SES-CD, endoscopic activity was classified as mucosal healing (0), endoscopic remission (0 to 2), mild activity (3 to 6), moderate activity (7 to 15), and severe activity (≥16). LRG levels were detected in all patients. Spearman rank correlation was used to analyze the correlation between LRG, clinical biochemical parameters and endoscopic scores. Receiver operating characteristic curve (ROC) was performed to determine the optimal cut-off value of LRG for evaluating endoscopic mucosal healing. Mann-Whitney U test, Kruskal-Wallis H test, and Bonferroni corrected test were used for statistical analysis. Results:Among 139 patients with small bowel CD, the LRG level was 17.3 (13.0, 25.2) mg/L, and SES-CD was 5 (1, 9); 32 patients achieved mucosal healing, 50 patients achieved endoscopic remission; 39 patients had mild activity, 40 patients had moderate activity, and 10 patients had severe activity. The SES-CD was negatively correlated with both hemoglobin and albumin ( r=-0.177 (95% confidence interval, 95% CI: -0.334 to -0.011), -0.293 (95% CI: -0.438 to -0.133)), with statistical significance ( P=0.037, <0.001). The SES-CD was positively correlated with CRP, CDAI, LRG and FC ( r=0.344 (95% CI: 0.188 to 0.482), 0.429 (95% CI: 0.282 to 0.556), 0.525 (95% CI: 0.393 to 0.636), 0.661 (95% CI: 0.556 to 0.745)), with statistical significant (all P<0.001). For the 64 small bowel CD patients with CRP in the normal reference value, SES-CD was positively correlated with CDAI, LRG and FC ( r=0.296 (95% CI: 0.054 to 0.505), 0.364 (95% CI: 0.129 to 0.559), 0.547 (95% CI: 0.348 to 0.699)), with statistical significance ( P=0.017, =0.003, <0.001). The LRG level of patients with endoscopic mucosal healing was significantly lower than that of patients with endoscopic remission (11.5 (10.1, 17.2) mg/L vs. 17.3 (13.4, 23.5) mg/L), with statistical significance ( Z=-3.25, P<0.001). ROC analysis showed that the area under the curve (AUC) of LRG in predicting endoscopic mucosal healing was 0.81 (95% CI: 0.73 to 0.89), with an optimal cut-off value of 15.27 mg/L. The sensitivity, specificity, positive predictive value and negative predictive value were 0.757, 0.718, 0.900 and 0.469, respectively. The accuracy of the combination of LRG and FC (AUC was 0.88, 95% CI: 0.82 to 0.94) in predicting endoscopic mucosal healing was higher than that of LRG alone (AUC was 0.81, 95% CI: 0.73 to 0.89), and the difference was statistically significant ( P=0.011). Conclusion:Based on the results of DBE, LRG may be a reliable biomarker for predicting endoscopic remission and mucosal healing in patients with small bowel CD.

To summarize the clinical practice of continuous medical service for patients at Shanghai Children′s Medical Center, affiliated with Shanghai Jiao Tong University School of Medicine, from September 2023 to December 2024, following the approval of its extended care qualification. This study utilized a mixed-methods research design that integrates quantitative and qualitative approaches. The quantitative study included a total of 117 subjects, with an age range of 18 to 35 years, an average age of 21.56 years, and a median age of 19 years; there were 59 males and 58 females. The disease types covered four major categories: childhood leukemia and solid tumors (68 cases), congenital structural malformations (25 cases), congenital hereditary metabolic diseases (4 cases), and rare diseases (20 cases). Among the subjects, 57.26% (67 cases) were first-time visitors to SCMC. The patients came from 20 provinces, autonomous regions, and municipalities across the country, with 88.03% (103 cases) from outside Shanghai. The treatment outcomes showed improvement or cure in 80.34% (94 cases) of the subjects, and there were no medical complaints. In addition, a qualitative study was conducted to deeply explore the experiences, confusions, and challenges of receiving or implementing continuous medical services from the perspectives of patients and their families, as well as medical staff. According to the inclusion and exclusion criteria, a total of 44 subjects were included in the study, among them, there were 12 patients, 12 family members who were taking care of the patients in SCMC, and 20 corresponding medical staff members. The results of the qualitative study showed that trust in the attending physicians of the children′s specialty hospital, a good doctor-patient relationship, satisfactory treatment outcomes, and support from medical insurance policies are the main driving forces for patients over 18 years old to receive continuous treatment at children′s specialty hospitals. The medical staff of the hospital also believed that this model can promote patient benefits. In conclusion, under the policy support of the Shanghai Municipal Health Commission, the "Six Fixed" Model for continuous treatment established by SCMC has achieved certain positive results in practice. This provides practical references for the development of continuous treatment in China and offers new strategies for the application of preventive medicine in the field of children′s health.

Lean six sigma (LSS) emphasizes patient demand-oriented, and continuously optimizing prolesses to achieve efficiency and standardization in medical services. Starting in 2021, a specialized children′s hospital (comprising one main campus and three branch campuses) introduced the LSS management method. Through define, measure, analyze, improve, and control, the hospital utilized tools such as questionnaire surveys, SIPOC models, and fishbone diagrams to identify pain points in the building of multi-campus child-friendly hospital and develop improvement measures.To address the main issues of insufficient cross-campus collaboration, significant disparities in service quality among different campuses, unclear functional positioning of each campus, and inadequate integration with social security mechanisms, the hospital implemented a " homogeneity-differentiation-coordination" management mode. The hospital implemented unified diagnosis and treatment standards and clinical pathways, carried out remote consultations and expert rotations, completed mutual recognition of examination and testing results, optimized child friendly labeling and child friendly environment, and built a " hospital-to-hospitals, hospital to medical schools, and hospital to community health centers" linkage platform, etc., to promote the standardization of diagnosis and treatment processes, clear functional positioning of the hospital area, and efficient resource allocation. Through practice, the outpatient appointment rate and patient satisfaction rate in the main hospital had increased from 86.72% and 98.64% in January December 2021 to 91.87% and 99.72% in January December 2024, respectively; The patient waiting time had been shortened from 26.54 minutes to 21.94 minutes, and the efficiency of medical treatment and service experience had been significantly improved. As of 2024, mutual recognition of 214 inspection and testing items had been achieved cross hospital campuses, forming a collaborative pattern of " main hospital leading, campuses support, and resource complementarity", and significantly improving the level of collaboration and child friendly connotation among multi-campus. This practice explored the integration path of multi-campus collaborative governance and child-friendly services, which could provide reference and inspiration for the similar hospitals.