Assessing the anti-inflammatory effects of whole-body vibration: a meta-analysis based on pre-clinical and clinical evidences.

BACKGROUND: Whole-body vibration (WBV) is a commonly used physical exercise for disease prevention and rehabilitation. Recent studies indicated the beneficial mechanism of WBV may be associated with its anti-inflammatory potential, however, its regulatory roles on different inflammatory mediators remained controversial. The aim of this study was to perform a meta-analysis to re-confirm the effects of WBV exercise on various inflammatory factors. METHODS: The PubMed, EMBASE and Cochrane Library databases were searched up to September 2023 to collect all articles comparing WBV with control (or post-pre trials). The effect size was expressed as the standardized mean difference (SMD) and 95% confidence intervals (CI). RESULTS: A total of 31 eligible studies were included, including 14 pre-clinical and 17 clinical studies. The meta-analysis of pre-clinical studies showed that compared with the control group, WBV exercise could significantly reduce the level of IL-6 (SMD: -1.03, 95% CI: -1.93, -0.13), TNF-α (SMD: -1.36, 95% CI: -2.54, -0.17) (for disease subgroup), IL-1ß (SMD: -2.20, 95% CI: -3.24, -1.15), IFN-γ (SMD: -1.91, 95% CI: -2.71, -1.12), IL-4 (SMD: -0.71, 95% CI: -1.39, -0.03) and IL-17 (SMD: -1.32, 95% CI: -2.05, -0.59) overall. Pooling of clinical studies revealed WBV exercise significantly reduced the level of TNF-α (WBV vs control: SMD: -1.11, 95% CI: -2.16, -0.06; post vs pre: SMD: -1.29, 95% CI: -1.91, -0.67), CRP (SMD: -3.59, 95% CI: -6.36, -0.82, P = 0.011) and enhanced the level of IL-10 (WBV vs control: SMD: 2.90, 95% CI: 1.10, 4.71; post vs pre: SMD: 1.75, 95% CI: 0.64, 2.87) and IL-6 (SMD: 0.91, 95% CI: 0.31, 1.52) (healthy subgroup). CONCLUSION: WBV may be an effective prevention and rehabilitation tool for inflammatory diseases.

Reevaluating elevated HDL cholesterol levels in healthy older persons as a risk factor for various disease states.

This article reviews the role of high-density lipoprotein cholesterol (HDL-C) in the elderly population, questioning the established view that advocates the ubiquitous health benefits of HDL cholesterol. High levels of HDL-C have been found to be associated with an increased risk of debilitating fractures, dementia, and cardiovascular disease, predominantly affecting older men, through the use of large population-based studies such as the ASPREE trial and the UK Biobank. Possible mechanisms are closely linked to cholesterol crystallization and altered HDL particle function. These findings call for a refinement of the understanding of high-density lipoprotein cholesterol (HDL-C), which implies adjustments to clinical guidelines and risk assessment strategies in older populations.

Spatiotemporal variations and driving factors of global terrestrial vegetation productivity gap under the changing of climate, CO2, landcover and N deposition.

Understanding the gap between potential productivity and the actual productivity of vegetation (vegetation productivity gap, VPG) is the basis to explore the potential productivity improvement and identify its constraints. In this study, we used the classification and regression tree model to simulate the potential net primary productivity (PNPP) based on the flux-observational maximum net primary productivity (NPP) across different vegetation types, that is, potential productivity. The actual NPP (ANPP) is obtained from the grid NPP averaged over five terrestrial biosphere models, and the VPG is subsequently calculated. On this basis, we used the variance decomposition method to separate the effects of climate change, land-use change, CO2, and nitrogen deposition on the trend and the interannual variability (IAV) of VPG from 1981 to 2010. Meanwhile, the spatiotemporal variation characteristics and influencing factors of VPG under future climate scenarios are analyzed. The results showed an increasing trend in PNPP and ANPP, while there was a decreasing trend of VPG in most parts of the world and this trend is more significant under representative concentration pathways (RCPs). The turning points (TP) of VPG variation are found under RCPs and the reduction trend of VPG before TP is more than that after TP. The VPG reduction in most regions was caused by the combined effects of PNPP and ANPP (41.68 %) from 1981 to 2010. However, the dominant factors of global VPG reduction are changing under RCPs, and the increment of NPP (39.71 % - 49.3 %) has become the dominating factor of VPG variation. CO2 plays a decisive role in the multi-year trend of VPG, while climate change is the main factor determining the IAV of VPG. Under changing climate, temperature and precipitation are negatively correlated with VPG in most parts of the world, and the relationship between radiation and VPG from weak negative to positive correlation.

Comprehensive analysis of immune-related genes for classification and immune microenvironment of asthma.

OBJECTIVES: To determine the effects of immune-related genes (IRGs) and immune landscape of induced sputum, and develop novel, non-invasive diagnostic molecular therapeutic targets for asthma. METHODS: GSE76262 datasets were used to identify differentially expressed IRGs in asthma. Key IRGs were detected using a protein-protein interaction network. Receiver operating characteristic (ROC) curves were analyzed to investigate the diagnostic value of key IRGs. Gene set enrichment analysis (GSEA) was performed with WebGestalt. Single-sample gene set enrichment analysis and CIBERSORT were used to investigate the immune landscape of induced sputum. RESULTS: A total of 75 potential IRGs were associated with asthma, most of which were involved in the NF-kappa B signaling pathway. ROC analysis showed AUC values for the hub pathway ranging from 0.676-0.767, with moderate diagnostic value for asthma. We also identified IRGs-related cytokines (TNF-α, IL-1ß, IL-8 and IL-6) in 76 asthma and 91 control serum samples to further explore diagnostic efficacy, showing a cumulative AUC of 0.998 for these four related cytokines. Analysis of immune cell infiltration levels showed that follicular helper T cells, activated dendritic cells, activated mast cells and eosinophils were significantly higher and macrophages M0 and macrophages M2 were significantly reduced in sputum from patients with asthma. CONCLUSIONS: IRGs-related cytokines and immune infiltration may contribute to the diagnosis and immune classification of asthma.

Research on task allocation of UAV cluster based on particle swarm quantization algorithm.

For the UAV cluster task allocation problem, the particle swarm optimization algorithm has slow convergence speed, low fitness level, easy to fall into local minimum, and can not obtain the global optimal solution. Aiming at the shortcomings of the traditional particle swarm optimization algorithm, a quantized particle swarm optimization algorithm (named QPSO method) has been designed to adapt to the task allocation problem of UAV cluster in this paper. In this algorithm, the Schrodinger equation is used to construct the quantized particle motion rule, and the Monte Carlo method is used to construct the update mechanism of the quantized particle position. The experimental results show that in the three groups of experiments of reconnaissance, attack and damage, the proposed algorithm has high adaptability, fast convergence speed, reasonable task allocation of UAVs in the cluster, efficient use of UAVs, and the performance of QPSO algorithm is obviously better than the particle swarm optimization algorithm and the genetic algorithm.

Identification of potential key autophagy-related genes in asthma with bioinformatics approaches.

OBJECTIVES: Asthma is a chronic respiratory disease characterized by airway remodeling and inflammation. Recent studies have demonstrated that multiple autophagy-related genes are involved in the pathogenesis of asthma. However, the roles of many of these autophagy-related genes in asthma remain unclear, particularly with regard to the diagnosis of asthma. METHODS: In this study, autophagy-related differentially expressed genes (DEGs) in asthma were identified by bioinformatics analysis of the GSE76262 datasets. Hub genes were screened by protein-protein interaction (PPI) network and module analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to explore potential signaling pathways. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of autophagy-related biomarkers in asthma. RESULTS: A total of 17 autophagy-related DEGs were identified, most of which were involved in autophagy and protein processing in the endoplasmic reticulum signaling pathway. ROC curve analysis demonstrated that the hub genes (HIF1A, ERN1, and DNAJB1) identified from the PPI network exhibited good performance in the diagnosis of asthma. The GSE137268 and GSE43696 databases were used to verify the expression of 17 autophagy-related DEGs in asthma. Interestingly, ERN1 was an overlapping gene defined by the intersection of hub autophagy-related DEGs and key modules (including HIF1A, ERN1, and DNAJB1). We also analyzed the interaction between miRNAs and mRNAs for 14 autophagy-related DEGs with an area under the curve > 0.7. The identified genes were involved in the glypican, interferon-gamma, and plasma membrane estrogen receptor signaling pathways. CONCLUSIONS: The results of this study indicate that specific signaling pathways and autophagy-related DEGs are potential diagnostic biomarkers related to the inception and progression of asthma.