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: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 explore the influence of different scanning positions based on chest phantom of computed tomography(CT)scan on chest on image quality and radiation dose.Methods:A thermoluminescent dosimeter(TLD)was placed at the breast area of simulating anthropoid chest phantom.GE Revolution evo CT was used to conduct scan on the conventional supine position(supine group)and prone position(prone group)for chest phantom.Different noise indexes(NI=10-23)were adjusted to control ration doses,and other parameters were fixed,and each group collected 12 sequence images.The average value(AV),standard deviation(SD)of the CT scan at region of interest(ROI)under different scanning positions were recorded to calculate the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of the image.The radiation dose at the breast area was measured by TLD,and the volume CT dose index(CTDIvol)and dose-length product(DLP)were recorded.Results:Under different scanning positions,the radiation dose of breast organs in the prone group was lower than that in the supine group,there was a statistically significant difference between the two groups(t=6.57,P<0.05),while there were not statistically significant differences in CTDIvol and DLP between the two groups(P>0.05).There were not statistically significant differences in the CT values,SD,SNR,CNR of lung tissue,and the CT values of breast tissue between the two groups of images(P>0.05).The SD,SNR and CNR of breast tissue in the prone group were lower than those in the supine group,and the differences were statistically significant(t=-13.33,-10.59,6.70,P<0.05).There were no statistically significant differences in the subjective scores of the clarity of the edge of the tissue within lung,the layers of soft tissue of the breast,noise,and artifacts in the bone tissue between the two groups of images(P>0.05).Conclusion:When low-dose CT physical examination on chest is conducted in clinical practice,the scanning of prone position during undergoing CT scan on chest can obtain image quality that can meet the requirements in diagnosing lung,and reduce the radiation dose on the breast,and conform to the technical principle of optimal radiation protection.

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 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 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.

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 investigate the current status and influencing factors of quality of life among HIV/AIDS cases, so as to provide the basis for improving HIV/AIDS cases quality of life. Methods: From March to July 2024, HIV/AIDS cases under follow-up management at various community health service centers in Jinshan District, Shanghai Municipality, were selected as the survey subjects using a convenience sampling method. Demographic information and receiving antiretroviral therapy (ART) were collected through questionnaire surveys. Quality of life was assessed using the Chinese version of the World Health Organization Quality of Life Questionnaire for HIV brief version. A multiple linear regression model was employed to analyze the factors affecting quality of life. Results: A total of 179 HIV/AIDS cases were investigated, including 150 males (83.80%) and 29 females (16.20%), with a mean age of (47.00±12.90) years. The subjective self-evaluation score for the quality of life among HIV/AIDS cases was (13.87±2.84) points. The scores in the domains of physical, psychological, independence, social relationship, environment, and spiritual support/religion/personal beliefs were (14.77±2.64) (13.57±2.04) (13.86±2.04) (12.99±2.26) (13.58±1.98) (14.59±3.05) points, respectively. Multiple linear regression analysis revealed statistically significant associations (all P<0.05) between the following factors and quality of life domain scores: educational level (college degree or above, β' =0.162) and receiving ART (β' =-0.197) were associated with the subjective self-evaluation domain score; educational level (college degree or above, β' =0.186) and receiving ART (β' =-0.299) were associated with physical domain score; receiving ART (β' =-0.263) and symptoms related to sexually transmitted diseases (β' =-0.243) were associated with psychological domain score; occupation (retirees, β' =-0.191) and symptoms related to sexually transmitted diseases (β' =-0.220) were correlated with the independence domain score; annual household income per capita (≥30 000 yuan, β' =0.281) and receiving ART (β' =-0.299) were correlated with the social relationship domain score; educational level (college degree or above, β' =0.206) and receiving ART (β' =-0.285) were correlated with the environment domain score; and receiving ART (β' =-0.492) and duration since HIV confirmation (3 to <6 years, β' =0.233; ≥6 years, β' =0.161) were correlated with the spiritual support/religion/personal beliefs domain score. Conclusions The overall quality of life among HIV/AIDS cases in Jinshan District is relatively good, but the domains of psychological, independence, and social relationship were still room for improvement. It is mainly influenced by factors such as occupation, educational level, annual household income per capita, receiving ART, symptoms related to sexually transmitted diseases, and duration since HIV confirmation.

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.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.