Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To investigate the disease burden, clinical characteristics and independent risk factors affecting in-hospital outcomes of children with congenital heart disease (CHD) combined with heart failure (HF) in China.Methods:(1) Descriptive study: based on the global burden of disease study 2021, available data on children under 15 years of age with CHD and HF in China from 1990 to 2021 were collected. The prevalence and trends in different age subgroups (<1 year, 1-<2 years, 2-<5 years, 5-<10 years, 10-<15 years) were analyzed, and the annual percentage change (EAPC) was estimated using linear regression. (2) Retrospective cohort study: a total of 1 062 children with CHD and HF from a multicenter study on pediatric HF in China were included. The children were divided into two groups:<2 years group and 2-<18 years group. Data on demographics, clinical features, diagnosis, treatments, and in-hospital outcomes were analyzed. Mann-Whitney U test and chi-square test were used for group comparisons.Multivariable Logistic regression was applied to identify factors influencing outcomes (in-hospital mortality and adverse cardiovascular events). Results:(1) From 1990 to 2021, the number of children with CHD and HF in China increased from 333 000 (95% uncertainty interval ( UI) 271 000-405 000) to 368 000 (95% UI 296 000-459 000), a growth of 10.8% (95% UI 5.0%-16.6%). Concurrently the prevalence rate increased from 104.5 (95% UI 85.1-127.3) per 100 000 to 142.0 (95% UI 114.0-176.8) per 100 000, a growth of 35.9% (95% UI 28.7%-43.0%), with an EAPC of 1.5% (95% CI 1.2%-1.8%). Although the number of cases in the<1 year and 1-<2 years groups decreased by 41.0% and 25.6%, respectively, the prevalence in all age groups showed an upward trend:<1 year EAPC 0.6% (95% CI 0.5%-0.7%); 1-<2 years EAPC 0.9% (95% CI 0.8%-1.0%); 2-<5 years EAPC 1.2% (95% CI 1.0%-1.4%); 5-<10 years EAPC 1.5% (95% CI 1.2%-1.8%); 10-<15 years EAPC 2.1% (95% CI 1.9%-2.3%). (2) The multicenter study revealed that among 1 062 hospitalized children, 528 (49.7%) were male and 534 (50.3%) were female, with the age at admission of 5.4 (2.2,18.2) months. The majority of the children (77.9%, 827/1 062) were under 2 years of age, whereas 22.1% (235/1 062) were aged between 2-<18 years. Children with complex congenital heart defects accounted for the highest proportion (48.6%, 516/1 062), while those with isolated CHD made up 31.5% (335/1 062). Statistically significant differences were observed in several variables in demographics, clinical features, diagnosis, treatments, and outcomes between the two age groups (all P<0.05). The use of renin-angiotensin-aldosterone system inhibitors (41.1%, 436/1 062) and beta-blockers (8.7%, 92/1 062) was lower in hospitalized children with CHD and HF. Logistic regression identified complex CHD ( OR=7.73, 95% CI 2.24-26.63; OR=3.17, 95% CI 1.92-5.23), pulmonary hyperperfusion ( OR=2.15, 95% CI 1.01-4.18; OR=2.00, 95% CI 1.35-2.97), left ventricular ejection fraction<55% ( OR=2.13, 95% CI 1.08-4.21; OR=2.80, 95% CI 1.45-5.56), arterial oxygen partial pressure ( OR=0.99, 95% CI 0.98-0.99; OR=0.99, 95% CI 0.98-0.99), and serum calcium levels ( OR=0.31, 95% CI 0.17-0.58; OR=0.42, 95% CI 0.28-0.62) as independent risk factors for in-hospital mortality and cardiovascular events. Conclusions:The disease burden of CHD combined with HF in China has shown a continuous upward trend from 1990 to 2021, with higher growth rates in older age groups. Complex CHD, pulmonary hyperperfusion, left ventricular ejection fraction <55%, arterial oxygen partial pressure, and serum calcium concentration are independent risk factors for in-hospital mortality and cardiovascular events.

Objective:To evaluate the changes of right atrial volume and function in patients with atrial septal defect（ASD）by four-dimensional ultrasound automatic quantitative technology，and to explore the predictive value and clinical significance for ASD with pulmonary arterial hypertension（PAH）.Methods:Sixty-one patients with ASD and 32 healthy volunteers（control group）who attended Central China Fuwai Hospital of Zhengzhou University，Fuwai Central China Cardiovascular Hospital from March 2023 to April 2024 were prospectively collected，and classified ASD patients into ASD without PAH group（non-PAH group， n=30）and ASD with PAH group（PAH group， n=31）according to whether or not they had PAH，and obtained right atrial reserve，ductal，systolic longitudinal and circumferential strains（RASr，RAScd，RASct，RASr-c，RAScd-c，RASct-c），right atrial minimal，maximal，and presystolic volumes（RAVmin，RAVmax，and RAVpreA），and calculated right atrial total，passive，and active ejection fraction（RAEF，RAPEF，RAAEF）. The ultrasound parameters and clinical data were compared between groups. Pearson's linear correlation was applied to analyse the correlation between the parameters（RASr，RAA，PASP，NT-proBNP）and mean pulmonary artery pressure（mPAP）. ROC curves were plotted and the area under the curve（AUC）was calculated to analyse the value of the four-dimensional strain parameters of the right atrium in independently and jointly predicting the combination of ASD with PAH. Results:①Compared with the control group，RAVmin，RAVmax，and RAVpreA were elevated in the non-PAH and PAH groups，and the absolute values of RAPEF and RAScd were decreased，whereas the absolute values of RAEF，RAAEF，RASr，RASct，RASr-c，and RASct-c were elevated in the non-PAH group，and decreased in the PAH group（all P<0.05）；compared with the non-PAH group，only RAVmin，RAVmax，and RAVpreA were elevated in the PAH group，and the rest of the above parameters were reduced，with statistically significant differences（all P<0.05）. ②The right atrial four-dimensional strain parameter RASr had the highest predictive value for ASD combined with PAH，with an AUC of 0.876，a specificity of 83.3%，and a sensitivity of 77.4%，respectively. ③RASr was negatively correlated with mPAP（ r=-0.591， P<0.001）and RAA，PASP and NT-proBNP were positively correlated with mPAP（ r=0.539，0.697，0.616；all P<0.001）. ④The predictive value of RASr combined with RAA，PASP，and NT-proBNP was superior for ASD combined with PAH（AUC=0.933）. Conclusions:Four-dimensional ultrasound automatic quantitative technology can effectively evaluate the changes of right atrial volume and function in patients with ASD. ASD patients without PAH have increased right atrial reserve and pump function，and decreased right atrial pipeline function. The right atrial function of ASD patients with PAH is significantly reduced，and the impairment is more severe. Among the right atrial four-dimensional strain parameters，RASr has the highest efficacy in predicting ASD combined with PAH. Moreover，the combination of RASr with RAA，PASP，and NT-proBNP has the best predictive efficacy for ASD combined with PAH，which holds significant clinical value.

Aim To clarify the mechanism by which Qishen Yixin Granules improved microcirculation vas-cular endothelial dysfunction(VED)in mice,through activating the Nrf2/HO-1 signaling pathway to regulate oxidative stress.Methods C57 mice were randomly divided into six groups:blank group,model group,pos-itive drug group,and low-,medium-,and high-dose groups of Qishen Yixin Granules.The VED model was established by long-term infusion of Ang Ⅱ combined with a high-fat diet.Each treatment group received the corresponding drug intervention.After four weeks of drug intervention,cardiac function was assessed by echocardiography.Carstairs staining was used to ob-serve the formation of microthrombi in myocardial tis-sue.The micro vascular ischemia was evaluated by Hei-denhain staining.The ultrastructure of endothelial cells was observed by electron microscopy.The levels of EMPs,ROS,NO,ET-1,TF,TM,VWF,and TXA2 in serum were measured by ELISA.The expression levels of MDA,SOD,and GSH-Px in mouse heart tissue were determined by chemical methods.Cardiac microvascu-lar density and the expression of Nrf2,Keap1,and HO-1 proteins were detected by Immunohistochemical stai-ning.The protein expressions of Keap1,cytoplasmic Nrf2,nuclear Nrf2,and HO-1 in myocardial tissue were detected by Western blot.Results Qishen Yixin Granules could effectively improve the cardiac function of mice,alleviate the damage of endothelial cells and endothelial function.They could up-regulate serum NO levels and the activities of antioxidant enzymes SOD and GSH-Px,while down-regulating the expression of ROS and vascular inflammatory injury factors such as ET-1,VWF,TXA2,TF,TM,and EMPs.Qishen Yixin Granules also increased the positive counts of CD34,Nrf2,and HO-1,as well as microvessel density.Fur-thermore,they inhibited the expression of MDA,Keap1,and cytoplasmic Nrf2 protein in myocardial tis-sue,while increasing the expression of nuclear proteins HO-1 and Nrf2.Conclusions Qishen Yixin Granules may inhibit oxidative stress and inflammatory response by regulating the Nrf2/HO-1 signaling pathway,thereby improving vascular endothelial damage and cardiac function in VED mice.

Exosomal circular RNA(circRNAs)are pivotal in cancer biology,and tumor pathophysiology.These stable,non-coding RNAs encapsulated in exosomes participated in cancer progression,tumor growth,metas-tasis,drug sensitivity and the tumor microenvironment(TME).Their presence in bodily fluids positions them as potential non-invasive biomarkers,revealing the molecular dynamics of cancers.Research in exosomal circRNAs is reshaping our understanding of neoplastic intercellular communication.Exploiting the natural properties of exosomes for targeted drug delivery and disrupting circRNA-mediated pro-tumorigenic signaling can develop new treatment modalities.Therefore,ongoing exploration of exoso-mal circRNAs in cancer research is poised to revolutionize clinical management of cancer.This emerging field offers hope for significant breakthroughs in cancer care.This review underscores the critical role of exosomal circRNAs in cancer biology and drug resistance,highlighting their potential as non-invasive biomarkers and therapeutic targets that could transform the clinical management of cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.