OBJECTIVE To compare the efficacy and safety of evolocumab and probucol in patients with ultra-high-risk atherosclerotic cardiovascular disease （ASCVD） following percutaneous coronary intervention （PCI）. METHODS A retrospective analysis was conducted on 156 ultra-high-risk ASCVD patients who underwent PCI in our institution between January 1， 2023 and December 31， 2024. According to the lipid-lowering regimen， the patients were categorized into evolocumab group （ n ＝86） and probucol group （ n ＝70）. Changes in lipid parameters [total cholesterol （TC）， low-density lipoprot ein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， triglycerides， lipoprotein （a）， and lipid goal achievement rate ] ， inflammatory markers [interleukin-6 （IL-6） and C-reactive protein （CRP） ] ， and cardiac function indices （left ventricular ejection fraction， left ventricular end-systolic diameter， left ventricular end-diastolic diameter， and N-terminal pro-B-type natriuretic peptide） were compared between two groups at baseline and after 6 months of treatment. The incidence of adverse clinical events during treatment， including acute myocardial infarction， in-stent restenosis， acute heart failure， cerebral hemorrhage， and stroke， was also evaluated. RESULTS No statistically significant differences were observed between the two groups at baseline （ P ＞0.05）. After 6 months of treatment， both groups demonstrated significant improvements in lipid profiles （except HDL-C） and inflammatory markers compared to those at baseline （ P ＜0.05）. The evolocumab group exhibited greater reductions in TC， LDL-C， IL-6， and CRP， along with a higher lipid target achievement rate， compared with the probucol group （ P ＜0.05）. There were no statistically significant differences in the cardiac function-related indicators before and after treatment between the two groups， nor in the incidence of adverse events during the treatment （ P ＞0.05）. CONCLUSIONS For ultra-high-risk ASCVD patients after PCI， both of the above treatment options are associated with improvements in blood lipid and inflammatory response， with good safety during short-term follow-up. Evolocumab shows superior efficacy in TC， LDL-C and inflammatory markers reduction and lipid target achievement， compared to probucol.

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.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

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.

BACKGROUND:Nanocell vesicles possess functions such as re-epithelialization,antioxidation,anti-inflammation,and regulation of extracellular matrix remodeling.Meanwhile,apoptotic bodies have the immunomodulatory effects.Therefore,the combination of the two to form nanofusion vesicles can synergistically promote the healing of diabetic skin wounds.OBJECTIVE:To elucidate the impact of nanofusion vesicles on skin wound healing in a diabetic murine model.METHODS:(1)Material preparation and characterization:The primary bone marrow mesenchymal stem cells of C57BL/6J neonatal mice and the neutrophil apoptotic bodies of C57BL/6J mice were isolated and extracted.The nanofusion vesicles were prepared by micro-extrusion mechanism.(2)In vitro experiment:MTT assay was used to detect the proliferative effect of different concentrations of nanofusion vesicles on NIH-3T3 cells and human umbilical vein endothelial cells.Reactive oxygen species fluorescence probe was used to detect the antioxidant effect of nano-fusion vesicles on NIH-3T3 cells treated with hydrogen peroxide(H2O2).The inhibitory effect of nanofusion vesicles on RAW 264.7 macrophage inflammation induced by lipopolyside was detected by real-time quantitative RT-qPCR.(3)In vivo experiment:36 male C57BL/6J mice were employed to develop a murine model of diabetes mellitus.Following the successful induction of diabetes,two circular full-thickness wounds,each with a diameter of 6 mm,were created on either side of the diabetic mice's spine using a skin punch.The mice were divided into three groups by random number table method.The control group was injected with 0.1 mL of phosphate buffer solution.The nanovesicle group was injected with 0.1 mL nanovesicles(25 μg/mL).The nanofusion vesicle group was injected with 0.1 mL nanofusion(25 μg/mL)vesicles.After treatment for three consecutive days,the wound healing and histomorphological changes were observed.RESULTS AND CONCLUSION:(1)In vitro experiment:nanofusion vesicles,when administered at concentrations ranging from 0 to 100 μg/mL,exhibited no toxic effects and promoted the proliferation of NIH-3T3 and HUVEC cell lines.Notably,a concentration of 25 μg/mL nanofusion vesicle significantly enhanced the proliferation of NIH-3T3 cells.Furthermore,the survival rate of human umbilical vein endothelial cells was observed to increase in correlation with escalating concentrations of nanofusion vesicles.Nanofusion vesicles had a good antioxidant effect.In comparison to the H2O2 group,the fluorescence signal indicative of reactive oxygen species was progressively diminished in both the nanovesicle group and the nanofusion vesicle group.Furthermore,nanofusion vesicles possessed anti-inflammatory capabilities,effectively mitigating the inflammatory response in macrophages triggered by lipopolysaccharide stimulation.(2)In vivo experiment:Hematoxylin-eosin and Masson's trichrome staining revealed that in comparison to the control group,both the nanovesicle group and the nanofusion vesicle group exhibited a significant increase in granulation tissue formation and collagen fiber deposition within the wounds by day 6.Notably,the nanofusion vesicle group displayed the most pronounced effects.On day 12,the wound of nanofusion vesicle group was significantly reduced,and the healing rate was significantly faster than that of other groups(P＜0.01),and the effect of promoting wound healing was the most significant.Our findings demonstrated that nanofusion vesicles exhibited superior pro-cell proliferative,antioxidant,and anti-inflammatory properties,thereby exerting a beneficial effect on the promotion of skin wound healing in diabetic mouse models.

BACKGROUND:Nanocell vesicles possess functions such as re-epithelialization,antioxidation,anti-inflammation,and regulation of extracellular matrix remodeling.Meanwhile,apoptotic bodies have the immunomodulatory effects.Therefore,the combination of the two to form nanofusion vesicles can synergistically promote the healing of diabetic skin wounds.OBJECTIVE:To elucidate the impact of nanofusion vesicles on skin wound healing in a diabetic murine model.METHODS:(1)Material preparation and characterization:The primary bone marrow mesenchymal stem cells of C57BL/6J neonatal mice and the neutrophil apoptotic bodies of C57BL/6J mice were isolated and extracted.The nanofusion vesicles were prepared by micro-extrusion mechanism.(2)In vitro experiment:MTT assay was used to detect the proliferative effect of different concentrations of nanofusion vesicles on NIH-3T3 cells and human umbilical vein endothelial cells.Reactive oxygen species fluorescence probe was used to detect the antioxidant effect of nano-fusion vesicles on NIH-3T3 cells treated with hydrogen peroxide(H2O2).The inhibitory effect of nanofusion vesicles on RAW 264.7 macrophage inflammation induced by lipopolyside was detected by real-time quantitative RT-qPCR.(3)In vivo experiment:36 male C57BL/6J mice were employed to develop a murine model of diabetes mellitus.Following the successful induction of diabetes,two circular full-thickness wounds,each with a diameter of 6 mm,were created on either side of the diabetic mice's spine using a skin punch.The mice were divided into three groups by random number table method.The control group was injected with 0.1 mL of phosphate buffer solution.The nanovesicle group was injected with 0.1 mL nanovesicles(25 μg/mL).The nanofusion vesicle group was injected with 0.1 mL nanofusion(25 μg/mL)vesicles.After treatment for three consecutive days,the wound healing and histomorphological changes were observed.RESULTS AND CONCLUSION:(1)In vitro experiment:nanofusion vesicles,when administered at concentrations ranging from 0 to 100 μg/mL,exhibited no toxic effects and promoted the proliferation of NIH-3T3 and HUVEC cell lines.Notably,a concentration of 25 μg/mL nanofusion vesicle significantly enhanced the proliferation of NIH-3T3 cells.Furthermore,the survival rate of human umbilical vein endothelial cells was observed to increase in correlation with escalating concentrations of nanofusion vesicles.Nanofusion vesicles had a good antioxidant effect.In comparison to the H2O2 group,the fluorescence signal indicative of reactive oxygen species was progressively diminished in both the nanovesicle group and the nanofusion vesicle group.Furthermore,nanofusion vesicles possessed anti-inflammatory capabilities,effectively mitigating the inflammatory response in macrophages triggered by lipopolysaccharide stimulation.(2)In vivo experiment:Hematoxylin-eosin and Masson's trichrome staining revealed that in comparison to the control group,both the nanovesicle group and the nanofusion vesicle group exhibited a significant increase in granulation tissue formation and collagen fiber deposition within the wounds by day 6.Notably,the nanofusion vesicle group displayed the most pronounced effects.On day 12,the wound of nanofusion vesicle group was significantly reduced,and the healing rate was significantly faster than that of other groups(P＜0.01),and the effect of promoting wound healing was the most significant.Our findings demonstrated that nanofusion vesicles exhibited superior pro-cell proliferative,antioxidant,and anti-inflammatory properties,thereby exerting a beneficial effect on the promotion of skin wound healing in diabetic mouse models.

ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

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.

ObjectiveTo construct a machine learning model combining two-dimensional shear wave elastography （2D-SWE） and serological markers， and to investigate its clinical value in differentiating primary biliary cholangitis （PBC） from overlap syndrome （OS）. MethodsA total of 199 patients with pathologically confirmed PBC or OS in Nantong Third People’s Hospital from January 2021 to December 2025 were retrospectively enrolled， with 125 patients in the PBC group and 74 in the OS group. The patients were randomly divided into a training set with 139 patients and a test set with 60 patients at a ratio of 7∶3. Related data were collected， including serological markers， conventional two-dimensional ultrasound parameters， and 2D-SWE parameters （including velocity of shear wave ［VS］ and liver fibrosis index ［LFI］）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The univariate and multivariate Logistic regression analyses were used to identify independent predictive factors， which were then incorporated into six machine learning models， and 5-fold cross-validation was used for optimization of parameters. The indicators including the area under the receiver operating characteristic curve （AUC） were compared in the test set to determine the best model， and the SHapley additive exPlanations （SHAP） analysis was used for model interpretation. ResultsFemale patients accounted for 87.8% in the OS group and 79.2% in the PBC group. Compared with the PBC group， the OS group had significantly higher age， VS， LFI， splenic area， aspartate aminotransferase， total bilirubin， prothrombin time， immunoglobulin G （IgG）， and immunoglobulin M （Z=-2.883， -6.524， -4.000， -3.061， -2.194， -2.372， -4.079， -6.964， and -2.709， all P<0.05） and a significantly lower platelet count （Z=4.098， P<0.001）， and there were also significant differences between the two groups in the proportion of patients with positive anti-nuclear antibody， fibrosis stage， and inflammation grade （χ²=3.458， 63.198， and 101.038， all P<0.05）. Variables without multicollinearity were included in the regression analysis， and the multivariate Logistic regression analysis showed that VS （odds ratio ［OR］=4.503， 95% confidence interval ［CI］： 1.698 — 11.943， P=0.003）， LFI （OR=1.813， 95%CI： 1.050 — 3.132， P=0.033）， and IgG （OR=1.121， 95%CI： 1.026 — 1.226， P=0.012） were independent predictive factors for differentiating PBC from OS. Six machine learning models were constructed based on these variables， among which the logistic regression model showed the best predictive performance in the test set， with an AUC of 0.881 （95%CI： 0.788 — 0.974）， a sensitivity of 0.731， and a specificity of 0.794. The SHAP analysis showed that IgG contributed the most to model prediction. ConclusionThe noninvasive multimodal machine learning model combining 2D-SWE parameters （VS， LFI） and serum IgG can effectively differentiate PBC from OS， providing a reference for clinical decision-making regarding the need for liver biopsy.