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.

Objective: To analyze adverse event following immunization(AEFI) with the domestic bivalent human papilloma virus (HPV) vaccine among female students in primary, junior, and senior high schools in Hebei Province from 2022 to 2024, aiming to provide evidence for the vaccination program. Methods: On May 14, 2025, AEFI case data and vaccination data for the domestic bivalent HPV vaccine among female students in primary, junior, and senior high schools in Hebei Province from 2022 to 2024 were collected from the China Disease Control and Prevention Information System and the Hebei Provincial Immunization Program Information Management System, respectively. Statistical analysis was performed by using descriptive and analytical epidemiological methods, with intergroup comparisons conducted by using the Chi square test or Fisher s exact probability test. Results: From 2022 to 2024, a total of 817 900 doses of the domestic bivalent HPV vaccine were administered to female students in primary, junior, and senior high schools in Hebei Province. A total of 68 AEFI cases were reported, comprising 57 common vaccine reactions, 7 rare vaccine reactions, 3 coincidental events, and 1 psychogenic reaction. The overall AEFI reporting rate was 8.31 per 100 000 doses, 4 cases (2.30/100 000 doses), 31 cases (8.73/100 000 doses), and 33 cases (11.41/100 000 doses) were reported in 2022, 2023, and 2024, respectively, with a statistically significant difference across years ( χ 2=10.97, P <0.01). The reporting rates for common and rare vaccine reactions were 6.97 per 100 000 doses and 0.86 per 100 000 doses, respectively. The AEFI reporting rates were 15.14 per 100 000 doses among primary school girls and 8.20 per 100 000 doses among secondary school girls, though the difference was not statistically significant (Fisher s exact test, P =0.30). The reporting rate was higher after the first dose (10.91/100 000 doses) than after the second dose (5.96/100 000 doses; χ 2=5.85, P =0.02).The second quarter recorded the highest AEFI reporting rate at 11.21 per 100 000 doses. The majority of AEFI cases (56 cases, 82.35%) occurred within 24 hours post vaccination. Conclusion The domestic bivalent HPV vaccine demonstrates a favorable safety profile among female students in Hebei Province, with a low overall AEFI reporting rate that consisted predominantly of general reactions and occasional cardiac related reactions.

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.

ObjectiveType I interferon (IFN-I) signaling is essential for antiviral innate immunity, yet its sustained or excessive activation contributes to the pathogenesis of several autoimmune diseases and interferonopathies, such as systemic lupus erythematosus and Aicardi-Goutières syndrome. Current strategies targeting this pathway, exemplified by JAK inhibitors, act mainly on downstream signal transduction and provide limited direct control over upstream IFN-I production, while also carrying the risk of broad immunosuppression. Phyllanthus emblica L. has long been used in traditional medicine for inflammatory disorders, but the bioactive constituent responsible for its regulation of IFN-I signaling and the underlying molecular mechanism have not been clearly defined. This study aimed to identify the active anti-inflammatory component of P. emblica and to characterize its mechanism of action on the IFN-I pathway in macrophages. MethodsActive components ofP. emblica and their candidate targets were screened by network pharmacology using the TCMSP and DrugBank databases (oral bioavailability≥30%, drug-likeness≥0.18) and intersected with inflammation-related genes retrieved from public databases. The predicted interaction between EGCG and IFN-I pathway proteins (TBK1, IRF3, STAT1) was evaluated by molecular docking, with BX795 and GSK8612 used as reference TBK1 inhibitors. Mechanistic experiments were performed in THP-1-derived macrophages and primary bone marrow-derived macrophages (BMDM). Upstream signaling was activated by transfection of the nucleic acid analogs poly(I∶C) and poly(dA∶dT) or by lipopolysaccharide (LPS) stimulation, whereas downstream signaling was activated by exogenous IFN-β. An siRNA-mediated TREX1 knockdown model was used to mimic endogenous nucleic acid-driven interferonopathy. Expression of IFN-β1 and interferon-stimulated genes (ISGs) was measured by RT-qPCR, protein phosphorylation by Western blot, and IFN-β secretion by ELISA. Cellular thermal shift assay (CETSA) and drug affinity responsive target stability (DARTS) were used to probe the interactionbetween EGCG and IRF3. ResultsNetwork pharmacology identified (-)-epigallocatechin-3-gallate (EGCG) as a candidate IFN-I-suppressive constituent of P. emblica, with predicted binding to TBK1, IRF3, and STAT1. Molecular docking yielded binding energies of -9.2, -7.2, and -8.2 kcal/mol for TBK1, IRF3, and STAT1, respectively, indicating an affinity for TBK1 comparable to that of the reference inhibitors BX795 (-5.7 kcal/mol) and GSK8612 (-6.4 kcal/mol). EGCG suppressed IFN-β1 and ISG mRNA expression under poly (I∶C), poly (dA∶dT), and LPS stimulation in both THP-1 macrophages and BMDM. At the protein level, EGCG reduced the phosphorylation of TBK1 and IRF3 without affecting the levels of the upstream sensors cGAS and RIG-I, and lowered IFN-β secretion in a concentration-dependent manner. CETSA and DARTS showed that EGCG did not enhance the thermal stability or protease resistance of IRF3, indicating that its effect on IRF3 is indirect. Following IFN-β stimulation, prolonged EGCG treatment reduced STAT1 phosphorylation in a time-dependent manner without an apparent change in IRF9, and partially attenuated ISG transcription; this effect was not monotonicly concentration-dependent, and CXCL10 showed the most consistent suppression. In TREX1-knockdown cells, the elevated mRNA levels of ISG15, ISG56, and CXCL10 were reduced by EGCG. ConclusionEGCG suppresses IFN-I responses by concurrently inhibiting TBK1-IRF3-dependent IFN‑β production and JAK-STAT1-mediated downstream transcription. These in vitro findings provide a mechanistic basis for the anti-inflammatory use of P. emblica in traditional medicine and identify EGCG as a candidate for further evaluation in interferon-driven autoimmune disease models.

AIM To optimize the extraction process for Jiangzhi Ligan Decoction,and to investigate its effects on improving lipid deposition in HepG2 cells.METHODS With extraction time,extraction frequency and solvent consumption as influencing factors,comprehensive score for tanshensu,lotine,catechin contents and extract yield as an evaluation index,the extraction process was optimized by Box-Behnken response surface method on the basis of single factor test.Palmitic acid-induced HepG2 cells were adopted in the establishment of non-alcoholic fatty liver disease model,after which the effect of drug-containing serum on TC and TG levels was investigated.RESULTS The optimal conditions were determined to be 90 min for extraction time,three times for extraction frequency,and 11 times for solvent consumption.Compared with the control group,the model group demonstrated increased TG,TC levels(P＜0.01);compared with the model group,the Jiangzhi Ligan Decoction high-dose group displayed decreased TG,TC levels(P＜0.05,P＜0.01),especially for those after optimization(P＜0.05,P＜0.01).CONCLUSION Jiangzhi Ligan Decoction can alleviate lipid deposition in HepG2 cells induced by palmitic acid,whose efficacy after extraction process optimization is better than that reported in literature.

Stem cell treatment may enhance erectile dysfunction (ED) in individuals with cavernous nerve injury (CNI). Nevertheless, no investigations have directly ascertained the implications of varying amounts of human umbilical cord-derived mesenchymal stem cells (HUC-MSCs) on ED. We compare the efficacy of three various doses of HUC-MSCs as a therapeutic strategy for ED. Sprague-Dawley rats (total = 175) were randomly allocated into five groups. A total of 35 rats underwent sham surgery and 140 rats endured bilateral CNI and were treated with vehicles or doses of HUC-MSCs (1 × 10 6 cells, 5 × 10 6 cells, and 1 × 10 7 cells in 0.1 ml, respectively). Penile tissues were harvested for histological analysis on 1 day, 3 days, 7 days, 14 days, 28 days, 60 days, and 90 days postsurgery. It was found that varying dosages of HUC-MSCs enhanced the erectile function of rats with bilateral CNI and ED. Moreover, there was no significant disparity in the effectiveness of various dosages of HUC-MSCs. However, the expression of endothelial markers (rat endothelial cell antigen-1 [RECA-1] and endothelial nitric oxide synthase [eNOS]), smooth muscle markers (alpha smooth muscle actin [α-SMA] and desmin), and neural markers (neurofilament [RECA-1] and neurogenic nitric oxide synthase [nNOS]) increased significantly with prolonged treatment time. Masson's staining demonstrated an increased in the smooth muscle cell (SMC)/collagen ratio. Significant changes were detected in the microstructures of various types of cells. In vivo imaging system (IVIS) analysis showed that at the 1 st day, the HUC-MSCs implanted moved to the site of damage. Additionally, the oxidative stress levels were dramatically reduced in the penises of rats administered with HUC-MSCs.
Male ; Animals ; Erectile Dysfunction/metabolism* ; Rats, Sprague-Dawley ; Mesenchymal Stem Cell Transplantation/methods* ; Rats ; Penis/pathology* ; Humans ; Disease Models, Animal ; Umbilical Cord/cytology* ; Peripheral Nerve Injuries/complications* ; Mesenchymal Stem Cells ; Nitric Oxide Synthase Type III/metabolism* ; Actins/metabolism* ; Nitric Oxide Synthase Type I/metabolism*

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

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.