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.

The lung collaterals form a network that branches from the lung meridian, traversing the lung system and extending across the body′s surface. Lung collateral disease refers to the structural alterations or dysfunction in these collaterals caused by external or internal pathogens. Research into the structural and physiological functions of children′s lung collaterals, as well as the pathogenesis and syndrome differentiation for treating lung collateral diseases in children, holds significant value in guiding the prevention and treatment of pediatric respiratory conditions. Drawing on the theory of collateral disease, the clinical insights of both historical and contemporary physicians, and modern research findings—while considering the unique physiological and pathological characteristics of children′s respiratory systems—this study provides a foundational summary of the morphology and spatial distribution of children′s lung collaterals. The characteristics of these collaterals are highlighted as thin, sparse, short, narrow, brittle, and tender. From this structural understanding, the unique physiological functions of children′s lung collaterals are analyzed. The study further explores the interactions between pathogenic factors and lung collaterals, elucidating the pathogenesis and progression of children′s lung collateral diseases. It proposes treatment principles centered on "seeking treatment in the collaterals and employing the method of unblocking collaterals, "which align with the unique features of pediatric lung collaterals. Common treatment approaches, and relevant prescriptions for managing these diseases are summarized. This paper lays the foundation for a theoretical system encompassing the structure, function, pathogenesis, and syndrome differentiation for treating children′s lung collateral diseases. It offers valuable insights for the clinical diagnosis and management of pediatric respiratory diseases linked to collateral dysfunction and serves as a reference for the systematic development of a broader theoretical framework for children′s collateral diseases.

It is considered that the fundamental pathogenesis of pediatric pertussis lies in the dysfunction of lung qi， and it is advocated to treat the disease with the method of regulating qi and relieving cough. Clinically， the disease is divided into three stages for syndrome differentiation and treatment， initial coughing stage， spasmodic coughing stage， and prolonged coughing stage. In the initial coughing stage， the pathogenesis involves invasion by external pathogens and failure of lung qi to disperse； the treatment principle is to release the exterior， expel pathogens， ventilate the lungs， and relieve cough. For cold patterns， modified San'ao Decoction （三拗汤） is prescribed； for heat type， a self-formulated Qingqi Xuanfei Decoction （清气宣肺汤） is used. In the spasmodic coughing stage， the pathogenesis is the congealing of phlegm and fire with impaired lung purification； the treatment focuses on eliminating phlegm， dredging the meridians， purging the lungs， and relieving cough. Mild cases are treated with a self-formulated Tongluo Xiefei Decoction （通络泻肺汤）， while severe cases are treated with a modified combination of Maxing Shigan Decoction （麻杏石甘汤） and Qianjin Weijing Decoction （千金苇茎汤）. In the prolonged coughing stage， the pathogenesis involves the depletion of qi and yin and latent pathogens in a weakened lung； the treatment aims to tonify qi， nourish yin， moisten the lungs， and eliminate residual pathogens. For lung yin deficiency， modified Shashen Maidong Decoction （沙参麦冬汤） is used； for lung-spleen qi deficiency， a self-formulated Jianpi Gufei Decoction （健脾固肺汤） is prescribed.

Objective: To investigate the assocation of sedentary behavior among college students on psychological health issues, such as depressive, anxiety, and stress symptoms, and to analyze the moderating role of takeaway consumption behavior in the context, in order to provide a scientific basis for reducing emotional symptoms among college students. Methods: A stratified cluster sampling method was employed to conduct a questionnaire on 3 427 first year students of a higher education institution in Hefei of Anhui Province from May to June 2021. The study variables included demographic characteristics, sedentary time, takeaway consumption behavior, and emotional (symptoms depressive, anxiety, and stress symptoms). The Spearman correlation analysis was used to analyze the association between variables, and linear regression analysis was used to analyze the association between takeaway consumption behavior and depressive, anxiety and stress symptoms among college students with sedentary time. Results: Both sedentary time and takeaway consumption behavior were positively correlated with depressive, anxiety and stress symptoms among college students ( r =0.10, 0.10, 0.10; 0.10, 0.11, 0.11, all P <0.05). The results of linear regression analysis showed that the interaction term between takeaway consumption behavior and sedentary time was positively correlated with symptoms of depressive, anxiety, and stress symptoms among college students (depression: β =0.04, anxiety: β =0.04, stress: β =0.04, all P <0.05). The results of the simple slope test demonstrated that regardless of the level of takeaway consumption behavior, sedentary time was positively correlated with the depressive symptoms of college students; compared with low takeaway consumption behavior, high takeaway consumption behavior ( β=0.77, P <0.01) enhanced the association between sedentary time and depressive symptoms among college students. In addition, under the condition of high takeaway consumption behavior, sedentary time was positively correlated with the anxiety and stress symptoms of college students (anxiety: β =0.64; stress: β =0.71, both P <0.01); while under the condition of low takeaway consumption behavior, sedentary time was not related to the anxiety and stress symptoms of college students ( β =0.17, 0.22, both P >0.05). Conclusions Sedentary behavior is related to a the emotional symptoms of depressive, anxiety, and stress among college students. Takeaway consumption behavior may exacerbate this impact.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Myocardial fibrosis is a serious cause of heart failure and even sudden cardiac death. However, the mechanisms underlying myocardial ischemia-induced cardiac fibrosis remain unclear. Here, we identified that the expression of sterile alpha and TIR motif containing 1 (SARM1), was increased significantly in the ischemic cardiomyopathy patients, dilated cardiomyopathy patients (GSE116250) and fibrotic heart tissues of mice. Additionally, inhibition or knockdown of SARM1 can improve myocardial fibrosis and cardiac function of myocardial infarction (MI) mice. Moreover, SARM1 fibroblasts-specific knock-in mice had increased deposition of extracellular matrix and impaired cardiac function. Mechanically, elevated expression of SARM1 promotes the deposition of extracellular matrix by directly modulating P4HA1. Notably, by using the Click-iT reaction, we identified that the increased expression of ZDHHC17 promotes the palmitoylation levels of SARM1, thereby accelerating the fibrosis process. Based on the fibrosis-promoting effect of SARM1, we screened several drugs with anti-myocardial fibrosis activity. In conclusion, we have unveiled that palmitoylated SARM1 targeting P4HA1 promotes collagen deposition and myocardial fibrosis. Inhibition of SARM1 is a potential strategy for the treatment of myocardial fibrosis. The sites where SARM1 interacts with P4HA1 and the palmitoylation modification sites of SARM1 may be the active targets for anti-fibrosis drugs.

Perimenopause raises the risk and incidence of depression, whereas the underlying molecular mechanism remains unclear. Disturbed glucose regulation has been widely documented in depressive disorders, which renders the brain susceptible to various stresses such as estrogen depletion. However, whether and how glucose dysfunction regulates depression-like behaviors and neuronal damage in perimenopausal transition remains unexplored. Here, a prominent depressive phenotype was found in perimenopausal mice induced by the ovarian toxin 4-vinylcyclohexene diepoxide (VCD). The VCD depression susceptible group (VCD^SS) and the VCD depression resilient group (VCD^RES) were determined using a ROC-based behavioral screening approach. We found that the hippocampus, a crucial region linked to depression, had hyperglycemia and mitochondrial abnormalities. Interestingly, oral administration of the SGLT2 inhibitor empagliflozin (EMPA) and intrahippocampal glucose infusion suggest a close relationship between hyperglycemia in the hippocampus and the susceptibility to depression. We verified that cytochrome c oxidase 7c (COX7C) downregulation is a potential cause of the high glucose-induced neuronal injury using proteomic screening and biochemical validations. High glucose causes COX7C to be ubiquitinated in a S-phase kinase associated protein 1 (SKP1)-dependent manner. According to these results, SKP1/COX7C represents a unique therapeutic target and a novel molecular route for treating perimenopausal depression.

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.

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.