Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

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.

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.

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.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

This study aimed to investigate the effect of Dahuang Zhechong Pills(DHZCP) in delaying heart aging(HA) and explore the potential mechanism. Network pharmacology and molecular docking were employed to explore the targets and potential mechanisms of DHZCP in delaying HA. Furthermore, in vitro experiments were conducted with the DHZCP-containing serum to verify key targets and pathways in D-galactose(D-gal)-induced aging of cardiomyocytes. Active components of DHZCP were searched against the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCSMP), and relevant targets were predicted. HA-related targets were screened from the GeneCards, Online Mendelian Inheritance in Man(OMIM), and DisGeNET. The common targets shared by the active components of DHZCP and HA were used to construct a protein-protein interaction network in STRING 12.0, and core targets were screened based on degree in Cytoscape 3.9.1. Metaspace was used for Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses of the core targets to predict the mechanisms. Molecular docking was performed in AutoDock Vina. The results indicated that a total of 774 targets of the active components of DHZCP and 4 520 targets related to HA were screened out, including 510 common targets. Core targets included B-cell lymphoma 2(BCL-2), serine/threonine kinase 1(AKT1), and hypoxia-inducible factor 1 subunit A(HIF1A). The GO and KEGG enrichment analyses suggested that DHZCP mainly exerted its effects via the phosphatidylinositol 3-kinase(PI3K)/AKT signaling pathway, HIF-1α signaling pathway, longevity signaling pathway, and apoptosis signaling pathway. Among the pathways predicted by GO and KEGG enrichment analyses, the PI3K/AKT/HIF-1α signaling pathway was selected for verification. The cell-counting kit 8(CCK-8) assay showed that D-gal significantly inhibited the proliferation of H9c2 cells, while DHZCP-containing serum increased the viability of H9c2 cells. SA-β-gal staining revealed a significant increase in the number of blue-green positive cells in the D-gal group, which was reduced by DHZCP-containing serum. TUNEL staining showed that DHZCP-containing serum decreased the number of apoptotic cells. After treatment with DHZCP-containing serum, the protein levels of Klotho, BCL-2, p-PI3K/PI3K, p-AKT1/AKT1, and HIF-1α were up-regulated, while those of P21, P16, BCL-2 associated X protein(Bax), and cleaved caspase-3 were down-regulated. The results indicated that DHZCP delayed HA via multiple components, targets, and pathways. Specifically, DHZCP may delay HA by reducing apoptosis via activating the PI3K/AKT/HIF-1α signaling pathway.
Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Signal Transduction/drug effects* ; Apoptosis/drug effects* ; Myocytes, Cardiac/cytology* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Animals ; Rats ; Humans ; Molecular Docking Simulation ; Aging/metabolism* ; Protein Interaction Maps/drug effects* ; Heart/drug effects* ; Network Pharmacology

This study explores the role and underlying molecular mechanisms of fucoidan sulfate(FPS) in regulating heme oxygenase-1(Hmox1)-mediated ferroptosis to ameliorate myocardial injury in diabetic cardiomyopathy(DCM) through in vivo and in vitro experiments and network pharmacology analysis. In vivo, a DCM rat model was established using a combination of "high-fat diet feeding + two low-dose streptozotocin(STZ) intraperitoneal injections". The rats were randomly divided into four groups: normal, model, FPS, and dapagliflozin(Dapa) groups. In vitro, a cellular model was created by inducing rat cardiomyocytes(H9c2 cells) with high glucose(HG), using zinc protoporphyrin(ZnPP), an Hmox1 inhibitor, as the positive control. An automatic biochemical analyzer was used to measure blood glucose(BG), serum aspartate aminotransferase(AST), serum lactate dehydrogenase(LDH), and serum creatine kinase-MB(CK-MB) levels. Echocardiography was used to assess rat cardiac function, including ejection fraction(EF) and fractional shortening(FS). Pathological staining was performed to observe myocardial morphology and fibrotic characteristics. DCFH-DA fluorescence probe was used to detect reactive oxygen species(ROS) levels in myocardial tissue. Specific assay kits were used to measure serum brain natriuretic peptide(BNP), myocardial Fe~(2+), and malondialdehyde(MDA) levels. Western blot(WB) was used to detect the expression levels of myosin heavy chain 7B(MYH7B), natriuretic peptide A(NPPA), collagens type Ⅰ(Col-Ⅰ), α-smooth muscle actin(α-SMA), ferritin heavy chain 1(FTH1), solute carrier family 7 member 11(SLC7A11), glutathione peroxidase 4(GPX4), 4-hydroxy-2-nonenal(4-HNE), and Hmox1. Immunohistochemistry(IHC) was used to examine Hmox1 protein expression patterns. FerroOrange and Highly Sensitive DCFH-DA fluorescence probes were used to detect intracellular Fe~(2+) and ROS levels. Transmission electron microscopy was used to observe changes in mitochondrial morphology. In network pharmacology, FPS targets were identified through the PubChem database and PharmMapper platform. DCM-related targets were integrated from OMIM, GeneCards, and DisGeNET databases, while ferroptosis-related targets were obtained from the FerrDb database. A protein-protein interaction(PPI) network was constructed for the intersection of these targets using STRING 11.0, and core targets were screened with Cytoscape 3.9.0. Molecular docking analysis was conducted using AutoDock and PyMOL 2.5. In vivo results showed that FPS significantly reduced AST, LDH, CK-MB, and BNP levels in DCM model rats, improved cardiac function, decreased the expression of myocardial injury proteins(MYH7B, NPPA, Col-Ⅰ, and α-SMA), alleviated myocardial hypertrophy and fibrosis, and reduced Fe~(2+), ROS, and MDA levels in myocardial tissue. Furthermore, FPS regulated the expression of ferroptosis-related markers(Hmox1, FTH1, SLC7A11, GPX4, and 4-HNE) to varying degrees. Network pharmacology results revealed 313 potential targets for FPS, 1 125 targets for DCM, and 14 common targets among FPS, DCM, and FerrDb. Hmox1 was identified as a key target, with FPS showing high docking activity with Hmox1. In vitro results demonstrated that FPS restored the expression levels of ferroptosis-related proteins, reduced intracellular Fe~(2+) and ROS levels, and alleviated mitochondrial structural damage in cardiomyocytes. In conclusion, FPS improves myocardial injury in DCM, with its underlying mechanism potentially involving the regulation of Hmox1 to inhibit ferroptosis. This study provides pharmacological evidence supporting the therapeutic potential of FPS for DCM-induced myocardial injury.
Animals ; Ferroptosis/drug effects* ; Rats ; Diabetic Cardiomyopathies/physiopathology* ; Male ; Rats, Sprague-Dawley ; Polysaccharides/pharmacology* ; Heme Oxygenase-1/genetics* ; Myocytes, Cardiac/metabolism* ; Myocardium/pathology* ; Humans ; Cell Line ; Heme Oxygenase (Decyclizing)

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*