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.

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.

Spatiotemporal variations and driving factors of global terrestrial vegetation productivity gap under the changing of climate, CO, 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.

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.

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.

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.