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:In alignment with the practical teaching objectives of Diseases of the Locomotor System and the competency requirements for graduates of military medical universities, this study conducted a multidimensional modification of the traditional objective structured clinical examination (OSCE) to provide a novel training mode that enhances the effectiveness of practical teaching of this course.Methods:The traditional OSCE was modified from the four dimensions of integration, individuality, immersion, and intelligence. The first three stations were set as "diagnosis" and the last three stations were set as "operation" to reflect the "integration" of diagnosis and treatment. The last station of "operation" was set as "medical cares of combat and training injuries" to reflect "individuality" of military medical training. The method of situation creation was used to reflect "immersion", and the application of intelligent terminals reflected "intelligence". The study involved 50 trainees who were assigned to either a modified OSCE group or a traditional teaching group through a random drawing process. The teaching effectiveness was assessed through evaluation of theoretical knowledge, practical skills, and participant satisfaction. The statistical analysis was conducted by SPSS 22.0, with parametric data assessed by t-tests and non-parametric data assessed by chi-square tests. Results:Before entering the department, there were no statistically significant differences in theoretical ( P=0.832) and practical ( P=0.513) scores between the two groups of trainees. However, after the internship, the modified OSCE group demonstrated significantly enhanced scores compared to the traditional teaching group, both in theory assessment [(93.88±1.92) vs. (90.76±2.85), P=0.001] and skill assessment [(94.32±1.25) vs. (91.68±2.82), P=0.001]. Additionally, the self-assessment of clinical capability improvement by the modified OSCE group was markedly higher across all dimensions than the traditional teaching group ( P=0.001). Furthermore, the evaluation conducted by basic combat unit on the job competency of graduated trainees indicated that the modified OSCE group outperformed the traditional teaching group in basic clinical diagnosis and treatment [(4.72±0.46) vs. (3.44±0.71), P=0.001], emergency management of combat and training injuries [(4.72±0.46) vs. (3.52±0.71), P=0.001], application of information technology [(4.44±0.71) vs. (3.91±0.80), P=0.029], basic military qualities [(4.40±0.71) vs. (3.92±0.91), P=0.043], mental health and resilience [(4.36±0.70) vs. (3.68±0.85), P=0.003], and the capacity for continuous learning [(4.64±0.70) vs. (3.83±0.76), P=0.001]. Conclusions:The modified OSCE teaching mode can better meet the practical teaching requirements for Diseases of the Locomotor System in the military medical university and thus holds promise for further application.

Objective To explore the value of 3D and 2D radiomics features models based on multiparameter MRI in predicting Ki-67 expression(with 14%and 20%as the critical values,respectively)in breast cancer.Methods The MRI images of 147 patients with pathologically confirmed Luminal breast cancer were analyzed retrospectively.The patients were randomly divided into training set and test set according to the ratio of 7︰3.The 3D and 2D radiomics features of intratumor and peritumor were extracted from diffusion weighted imaging(DWI),dynamic contrast enhancement(DCE)mask(S0)and DCE phase 3(S3)images.Then the models were constructed by multiple pipeline combinations of three feature normalization methods,two feature dimensionality reduction methods,four feature selection methods,and ten classifiers.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the prediction performance of the models in order to select the best 3D and 2D single parame-ter(DWI,S0,S3)and multiparameter combination(S0+S3,S0+DWI,S3+DWI,S0+S3+DWI)models.Finally,the differ-ences between the models were compared by De Long test.Results With 14%as the critical value,the AUC of 3D and 2D models in the training set were 0.726-0.824 and 0.707-0.835,respectively,and those in the test set were 0.724-0.82 and 0.701-0.805.With 20%as the critical value,the AUC of 3D and 2D models in the training set were 0.743-0.868 and 0.793-0.881,respectively,and those in the test set were 0.738-0.853 and 0.743-0.814.There was no significant statistical difference between 3D and 2D models with the same parameter in the two critical values standards.Conclusion The multiparameter MRI-based radiomics models can bet-ter predict the expression of Ki-67 in breast cancer,and the models based on intratumor and peritumor 3D and 2D features have the same prediction efficiency.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective To explore the risk factors of early hypocalcemia after endoscopic thyroidectomy by breast approach(ETBA)and establish a predictive model to evaluate its occurrence risk.Methods A total of 155 patients with thyroid nodules who underwent ETBA were selected.Patients were divided into the low calcium group(＜2 mmol/L,n=41)and the normal group(≥2 mmol/L,n=114)according to the serum calcium level 24 hours after the operation.Before the operation,thyroid function and parathyroid hormone(PTH)were detected,and ultrasound was performed to evaluate cervical lymph node enlargement.Meanwhile,nodule location,maximum tumor diameter,nodule adhesion to the capsule,calcification and the edge of the nodule were also detected.The surgical conditions such as gland resection(unilateral or bilateral),operation time and misresection of parathyroid glands were recorded.PTH and serum calcium were detected 24 hours after the operation.Pathological assessment was used to evaluate benign and malignant conditions,Hashimoto's thyroiditis,multifocal lesions,thyroid capsule invasion and lymph node metastasis.Results Compared with the normal group,the cervical lymph node metastasis,malignant nodules,multifocal lesions,cervical lymph node enlargement,bilateral gland resection,parathyroid gland resection by mistake,combined Hashimoto's thyroiditis,maximum tumor diameter and operation time were increased in the hypocalcemia group,but PTH at 24 hours after the operation was decreased(P＜0.05).Multivariate Logistic regression analysis showed that cervical lymph node metastasis,long operation time,parathyroid resection by mistake,combined Hashimoto's thyroiditis and maximum tumor diameter were independent risk factors for early hypocalcemia in ETBA.Based on this,a visual nomogram model was constructed,with excellent discrimination[the area under the receiver operating characteristic(ROC)curve was 0.920(95%CI:0.834-0.971)],and the calibration curve showed that the predicted values were highly consistent with the measured values(Hosmer-Lemeshow χ2=0.007,P=0.087).Conclusion The nomogram model constructed based on multivariate Logistic regression can effectively predict the risk of early hypocalcemia after ETBA.

Objective:Exploring the association between Life's Crucial 9 (LC9) and the risk of thyroid dysfunction (TD), as well as its potential predictive capacity.Methods:A total of 247 600 TD-free participants from the UK Biobank were enrolled in the study. The LC9 score was divided into three CVH groups: low (0-), medium (50-), and high (80-100). Cox proportional hazards regression models were used to calculate the HRs and 95% CIs of the risk of TD with LC9 CVH status. Calculate Harrell's concordance index ( C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to evaluate the predictive ability of the LC9 score and Life's Essential 8 (LE8) score. Results:During a median follow-up of 12.3 years, 5 515, 911, and 4 869 new cases of TD, hyperthyroidism, and hypothyroidism were documented, respectively. Participants with a high LE8 CVH group had 57.00% ( HR=0.43, 95% CI: 0.38-0.49), 55.00% ( HR=0.45, 95% CI: 0.34-0.60), and 58.00% ( HR=0.42, 95% CI: 0.37-0.47) lower risk of TD, hyperthyroidism, and hypothyroidism, respectively, than those with low CVH group. Compared with the LE8 score, the improvement in C-index for the LC9 score predicted TD risk was 0.004 (95% CI: 0.001-0.007), the NRI was 0.101 (95% CI: 0.021-0.103), and the IDI was 0.001 (95% CI: 0.000-0.001). Conclusions:The better CVH status, defined by LC9, was associated with a lower risk of TD. Compared to the LE8 score, the LC9 score demonstrated a significant enhancement in both risk discrimination and reclassification capability for TD risk.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

The in-depth research of artificial intelligence in the medical field has greatly improved the workflow and diagnostic ability of diagnostic radiology.This article focuses on artificial intelligence technology in the field of interventional medicine,and enumerates its potential application scenarios,including improving image analysis capabilities to assist diagnosis and predict treatment response.It also describes the challenges that need to be overcome for practical application.Finally,with the continuous development of artificial intelligence in interventional medicine,artificial intelligence will further optimize the channels of interventional medicine and bring revolutionary changes to the clinical practice of interventional medicine.

OBJECTIVE To investigate the effects of the split formulas of Biyuan Heji(BYHJ)on paranasal sinus mucosal in-flammation in rats with acute rhinosinusitis(ARS)based on the pathogenesis of"wind-cold transforming into lung-heat".METHODS Unilateral nasal cavity occlusion combined with nasal dripping of Staphylococcus aureus were performed to establish a rat model of ARS.SD rats were randomly divided into blank,model,BYHJ(wind-cold removal+lung-heat removal),lung-heat removal,wind-cold removal,and positive drug groups,with 6 rats in each group.The rats were treated with the corresponding drugs for 7 d and then the samples were collected.HE staining was used to observe the pathological changes of rat paranasal sinus mucosa tissues,ELISA was employed to determine the levels of interleukin-1β(IL-1β),IL-6,IL-8,IL-9,IL-10,and IL-12 in serum,immunohistochemis-try(IHC)was adopted to measure the protein expression of tumor necrosis factor-α(TNF-α)and intercellular adhesion molecule(ICAM-1)in paranasal sinus mucosa tissues,and Western blot was used to detect the protein expression of phosphorylated p38 mito-gen-activated protein kinase(p38 MAPK),nuclear transcription factor-κB p50(NF-κB p50),and NF-κB p65 in paranasal sinus mucosa tissues.RESULTS The acute sinusitis rat inflammation model was successfully established.Compared with the model group,the water drinking,diet eating,and body weight of rats in the BYHJ group,wind-cold removal,lung-heat removal,and positive drug groups were significantly improved,the aggregation of inflammatory cells in the paranasal sinus mucosal tissue was reduced,and the levels of IL-1β,IL-6,IL-8,IL-9,and IL-12 in the serum were significantly reduced(P<0.01),IL-10 content significantly in-creased(P<0.01),the protein expression of TNF-α,ICAM-1,p38 MAPK,NF-κB p50,and NF-κB p65 in the paranasal sinus mucosa was significantly decreased(P<0.01).The comparison between various traditional Chinese medicine groups showed that the decrease of IL-1β,IL-6,IL-8,IL-9,IL-12,TNF-α,ICAM-1,p38 MAPK,NF-κB p50,and NF-κB p65 and the increase of IL-10 in the BYHJ group were better than those in the split formula groups(P<0.01),and the lung-heat removal group was better than the wind-cold removal group(P<0.01).CONCLUSION BYHJ and its split formulas can effectively inhibit the inflammatory response in rats with ARS.