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 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.

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: 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: 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.

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

Sigma-1 receptor (σ ₁R) has become a focus point of drug discovery for central nervous system (CNS) diseases. A series of novel 1-phenylethan-1-one O-(2-aminoethyl) oxime derivatives were synthesized. In vitro biological evaluation led to the identification of 1a, 14a, 15d and 16d as the most high-affinity (K _i < 4 nmol/L) and selective σ ₁R agonists. Among these, 15d, the most metabolically stable derivative exhibited high selectivity for σ ₁R in relation to σ ₂R and 52 other human targets. In addition to low CYP450 inhibition and induction, 15d also exhibited high brain permeability and excellent oral bioavailability. Importantly, 15d demonstrated effective antipsychotic potency, particularly for alleviating negative symptoms and improving cognitive impairment in experimental animal models, both of which are major challenges for schizophrenia treatment. Moreover, 15d produced no significant extrapyramidal symptoms, exhibiting superior pharmacological profiles in relation to current antipsychotic drugs. Mechanistically, 15d inhibited GSK3β and enhanced prefrontal BDNF expression and excitatory synaptic transmission in pyramidal neurons. Collectively, these in vivo proof-of-concept findings provide substantial experimental evidence to demonstrate that modulating σ ₁R represents a potential new therapeutic approach for schizophrenia. The novel chemical entity along with its favorable drug-like and pharmacological profile of 15d renders it a promising candidate for treating schizophrenia.

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, metastasis, 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 exosomal 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.