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Objective: To investigate the role of 14-3-3β in acute asthma exacerbations in children and analyze the risk factors for asthma exacerbations., Methods: This study recruited 101 children with acute asthma exacerbations, 101 children with stable asthma, and 65 healthy children. Serum 14-3-3β was compared among the three groups. Factors such as asthma family history, skin prick test, serum-specific IgE test, coinfections, and clinical indicators (FeNO, FEV1, white blood cells, eosinophils, and serum IgE level) were compared between the asthma groups. Risk factors associated with acute asthma exacerbations were identified using multivariate logistic regression models. ROC curve was drawn to determine the diagnostic sensitivity and specificity of 14-3-3β., Results: Serum 14-3-3β was significantly greater in the acute asthma group than in the stable asthma and control groups. Serum 14-3-3β was higher in severe acute asthma group than in mild-moderate asthma group. There were no significant differences in serum 14-3-3β levels between stable asthma and control groups ( p  > .05). Multivariate logistic regression analysis revealed that serum 14-3-3β level, FeNO, coinfection, and FEV1 z-score significantly increased the odds of acute asthma exacerbations in children. The optimal 14-3-3β cutoff value (39.79 ng/mL), had a sensitivity of 69.3% and specificity of 94.1% for predicting acute asthma exacerbations., Conclusions: 14-3-3β is elevated in children with acute exacerbations of asthma, and increases with exacerbation severity. 14-3-3β, FeNO, FEV1, and coinfection could be independent risk factors for predicting asthma exacerbations. The optimal 14-3-3β cutoff value for predicting asthma exacerbations was 39.79 ng/mL.

Objective: To investigate the diagnostic efficacy of serum IL-33 single indicator and combined indicators for asthma in children., Methods: 132 children were initially diagnosed with asthma during acute exacerbation and 100 healthy children were included. Serum IL-33 concentration differences were compared between asthmatic and normal children. Correlations between IL-33 with pulmonary function parameters, FeNO, peripheral blood EOS counts and serum total IgE were analyzed in asthmatic children. ROC curves were used to assess IL-33 diagnostic efficacy and its combined indicators. To prevent overfitting of the predictive model, the hold-out cross-validation method was used., Results: (1) Serum IL-33 concentrations were significantly higher in children with asthma than in normal children (p < 0.001). (2) IL-33 concentration was negatively correlated with FVC z-score, FEV1 z-score and FEF75% z-score in asthmatic children (p < 0.05). (3) The area under the ROC curve of IL-33 was 0.821, which was higher than those of FeNO, FVC z-score, and FEV1 z-score. (4) Cross-validation of the combined indicators showed that IL-33 significantly improved asthma diagnostic efficacy. The combination of IL-33, FEF75% z-score, and FeNO showed the highest diagnostic efficacy, with the AUC, sensitivity, and specificity of the combined indicator being 0.954, 90.1%, and 89. 0%, respectively, and good extrapolation of the predictive model., Conclusion: Serum IL-33 is higher in children with asthma and increases with the severity of pulmonary ventilation obstruction. A single indicator of serum IL-33 demonstrates moderate diagnostic accuracy, and its combination with FEF75% z-score and FeNO significantly improves the diagnostic accuracy in childhood asthma., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (Copyright © 2023 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.)

Pyroptosis is a newly identified mode of programmed cell death, but the potential role in patients with acute myocardial infarction (AMI) remains unclear. In this study, bioinformatics methods were used to identify differentially expressed genes from peripheral blood transcriptome data between normal subjects and patients with AMI which were downloaded by the Gene Expression Omnibus database. Comparing Random Forest (RF) and Support Vector Machine (SVM) training algorithms were used to identify pyroptosis-related genes, predicting patients with AMI by nomogram based on informative genes. Moreover, clustering was used to amplify the feature of pyroptosis, in order to facilitate analysis distinct biological differences. Diversity analysis indicated that a majority of pyroptosis-related genes are expressed at higher levels in patients with AMI. The receiver operating characteristic curves show that the RF model is more responsive than the SVM machine learning model to the pyroptosis characteristics of these patients in vivo. We obtained a column line graph diagnostic model which was developed based on 19 genes established by the RF model. After the consensus clustering algorithm of single sample Gene Set Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, the results for them found that pyroptosis-related genes mediate the activation of multiple immune cells and many inflammatory pathways in the body. We used RF and SVM algorithms to determine 19 pyroptosis-related genes and evaluate their immunological effects in patients with AMI. We also constructed a series of by nomogram related to pyroptosis-related genes to predict the risk of developing AMI., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

This paper presents the preparation of aerogel/polytetrafluoroethylene (PTFE) microporous materials via needleless electrospray technique, by using an aqueous dispersion of polytetrafluoroethylene as the basic spinning liquid. Different contents of aerogel powders were applied to the spinning liquid for electrospraying to investigate the effect on the structural characteristics and various properties of the materials. Cross-section, surface morphology, and particle size distribution of the electrosprayed materials were examined. Surface roughness, hydrophobicity, and thermal conductivity were evaluated and discussed. The results showed that the electrosprayed aerogel/PTFE layers were compact and disordered stacking structures composed of spherical particles with a rough surface. As the aerogel content increased, the electrosprayed materials demonstrated increased surface roughness and improved surface hydrophobicity with a contact angle up to 147.88°. In addition, the successful achievement of thermal conductivity as low as 0.024 (W m -1 K -1 ) indicated a superior ability of the prepared aerogel/PTFE composites to prevent heat transfer. This study contributes to the field of development of aerogel/PTFE composites via electrospray technique, providing enhanced final performance for potential use as thermal and moisture barriers in textiles or electronic devices.

Aims: To explore the correlation between cardiac-related comorbidities, cardiac biomarkers, acute myocardial injury, and severity level, outcomes in COVID-19 patients., Method: Pubmed, Web of Science, Embase, CNKI, VIP, Wanfang, Cochrane Library databases, medRxiv, and Sinomed were reviewed systemically. Various types of clinical research reporting cardiac-related comorbidities, cardiac biomarkers including lactate dehydrogenase (LDH), troponin I (TnI), high sensitivity troponin I (hs-TnI), creatine kinase (CK), creatine kinase-MB (CK-MB), myoglobin (Myo), N-terminal pro-b-type natriuretic peptide (NT-proBNP) and acute cardiac injury grouped by severity of COVID-19 were included. Outcome measures were events and total sample size for comorbidities, acute cardiac injury, and laboratory parameters of these biomarkers. The study was performed with Stata version 15.1., Results: Seventy studies, with a total of 15,354 cases were identified. The results showed that COVID-19's severity was related to cardiovascular disease. Similar odds ratios (ORs) were achieved in hypertension except for severe versus critical group (OR = 1.406; 95% CI, 0.942-2.097; p = .095). The relative risk (RR) of acute cardiac injury is 7.01 (95% CI, 5.64-8.71) in non-survivor cases. When compared with the different severity of cardiac biomarkers, the pool OR of CK, CK-MB, TnI, Myo and LDH were 2.683 (95% CI, 0.83-8.671; p = .106; I 2  = 0%), 2.263 (95% CI, 0.939-5.457; p = .069), 1.242 (95% CI, 0.628-2.457; p = .534), 1.756 (95% CI, 0.608-5.071; p = .298; I 2  = 42.3%), 1.387 (95% CI, 0.707-2.721;  p = .341; I 2  = 0%) in the critical versus severe group, whose trends were not similar to other groups. The standard mean differences (SMD) of CK and TnI in the critical versus severe group were 0.09 (95% CI, -0.33 to 0.50; p = .685; I 2  = 65.2%), 0.478 (95% CI, -0.183 to 1.138; p = .156; I 2  = 76.7%), which means no difference was observed in the serum level of these indicators., Conclusion: Most of the findings clearly indicate that hypertension, cardiovascular disease, acute cardiac injury, and related laboratory indicators are associated with the severity of COVID-19. What is now needed are cross-national prospectively designed observational or clinical trials that will help improve the certainty of the available evidence and treatment decisions for patients., (© 2021 The Authors. Immunity, Inflammation and Disease published by John Wiley & Sons Ltd.)

This work is the first attempt to prepare microporous polytetrafluoroethylene (PTFE) fibrous layers embedded with aerogels/phase change materials. For preparation of this layer, the needle-less electrospray technology of water dispersion of individual components is used. Microstructure characteristics, including surface morphology and particle size distribution, and various properties of the prepared materials were investigated and explained. Transport performance of the fibrous layers embedded with aerogels/phase change materials, such as the transmission of heat, air, and water vapor was evaluated and discussed in details. It was found that the electro-sprayed materials composed by spherical particles with rough surface had compact disordered stacking structure. Aerogels and phase change materials (PCMs) play different roles in determining structural parameters and transport properties of the materials. Those parameters and properties could be flexibly adjusted by optimizing the spinning parameters, changing the content or proportion of the fillers to meet specific requirements.

Nowadays, fibrous polyester materials are becoming one of the most important alternatives for controlling reverberation time by absorbing unwanted sound energy in the automobile and construction fields. Thus, it is worthy and meaningful to characterize their acoustic behavior. To do so, non-acoustic parameters, such as tortuosity, viscous and thermal characteristic lengths and thermal permeability, must be determined. Representative panels of polyester fibrous material manufactured by perpendicular laying technology are thus tested via the Bayesian reconstruction procedure. The estimated porosity and airflow resistivity are found in good agreement with those tested via direct measurements. In addition, the homogeneity of polyester fibrous panels was characterized by investigating the mean relative differences of inferred non-acoustic parameters from the direct and reverse orientation measurements. Some parameters, such as tortuosity, porosity and airflow resistivity, exhibit very low relative differences. It is found that most of the panels can be assumed homogeneous along with the panel thickness, the slight inhomogeneity mostly affecting the thermal characteristic length.

Polyester nonwoven fibrous material is widely used in construction and automobile industries for thermal insulation purposes. It is worthy and meaningful to understand the effect of structural parameters on the thermal property. Fiber orientation, as one of the most vital parameters, has a significant effect on thermal property. However, there has been little quantitative analysis focusing on this aspect. This paper theoretically and experimentally analyzes the thermal conductivity of samples with varying fiber orientation. Existing models were selected to predict the thermal conductivity of polyester nonwoven samples. Two different apparatus were applied to carry out the experimental measurements. The relative differences between the predicted and measured results were compared. One commonly used model was modified for accurate prediction. It was shown that some existing models under- or overestimate the thermal conductivity compared to the measured values. The results indicate that the modified model can accurately predict the thermal conductivity of polyester nonwoven materials within a 0.2% relative difference.

Polytetrafluoroethylene (PTFE) is a synthetic fluoropolymer known for its excellent hydrophobic properties. In this work, samples from PTFE dispersions with different combinations of water and carbon microparticles were prepared using an electrospraying method. The morphologies and sizes of carbon particles were investigated and the properties of layers including roughness, hydrophobicity and electrical resistivity were investigated. The non-conductive carbon microparticles were selected as a model particle to check the compatibility and electrospraying ability, and it had no effect on the hydrophobic and electrical properties. Carbon microparticles in polymer solution increased the degree of ionization and was found to be beneficial for the shape control of materials. The results showed that PTFE dispersion with the composition of water and carbon microparticles produced fine sphere particles and the layer fabricated with increased roughness. It was also found that the electrical resistivity and hydrophobicity of all the layers comparatively increased. The fabricated microporous layers can be used in various applications like interlining layer in multilayer textile sandwiches.