Accurate Simulation for 2D Lubricating Materials in Realistic Environments: From Classical to Quantum Mechanical Methods.

2D materials such as graphene, MoS2, and hexagonal BN are the most advanced solid lubricating materials with superior friction and anti-wear performance. However, as a typical surface phenomenon, the lubricating properties of 2D materials are largely dependent on the surrounding environment, such as temperature, stress, humidity, oxygen, and other environmental substances. Given the technical challenges in experiment for real-time and in situ detection of microscopic environment-material interaction, recent years have witnessed the acceleration of computational research on the lubrication behavior of 2D materials in realistic environments. This study reviews the up-to-date computational studies for the effect of environmental factors on the lubrication performance of 2D materials, summarizes the theoretical methods in lubrication from classical to quantum-mechanics ones, and emphasizes the importance of quantum method in revealing the lubrication mechanism at atomic and electronic level. An effective simulation method based on ab initio molecular dynamics is also proposed to try to provide more ways to accurately reveal the friction mechanisms and reliably guide the lubricating material design. On the basis of current development, future prospects, and challenges for the simulation and modeling in lubrication with realistic environment are outlined.

Identification of pivotal genes and pathways in the synovial tissue of patients with rheumatoid arthritis and osteoarthritis through integrated bioinformatic analysis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the two most common debilitating joint disorders and although both share similar clinical manifestations, the pathogenesis of each is different and remains relatively unclear. The present study aimed to use bioinformatic analysis to identify pivotal genes and pathways involved in the pathogenesis of RA. Microarray datasets from patients with RA and OA were obtained from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified using GEO2R software; Gene Ontology analysis and pathway enrichment were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the Kyoto Encylopedia for Genes and Genomes, respectively; and protein­protein interaction networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes database, and module analysis and pathway crosstalk of the PPI network was visualized using plugins of Cytoscape. In addition, the prediction of target mRNAs for differentially expressed microRNAs (DEMs) was performing using the starBase database and the identified pivotal genes were verified using reverse­transcription quantitative PCR in synovial tissue from patients with RA. A total of 566 DEGs were identified in GSE55457, GSE55235 while 23 DEMs were identified in the GSE72564 dataset. Upregulated DEGs were found to be mostly enriched in the 'Cytokine­cytokine receptor interaction' pathway, whereas downregulated DEGs were discovered to be enriched in the 'PPAR signaling pathway'. The top 25 DEGs were mostly enriched in the 'Chemokine signaling pathway'. In addition, six of the miRNA target genes were selected as potential biomarkers and a total of 24 genes were selected as potential hub genes. Experimental validation demonstrated that the expression levels of Cytotoxic T­Lymphocyte Associated Protein 4 (CTLA4), Zeta­chain­associated protein kinase 70 (ZAP70) and LCK proto­oncogene (LCK) were significantly increased, whereas HGF expression levels were decreased in RA synovial tissue. In conclusion, these findings suggest that the identified DEGs and pivotal genes in the present study may further enhance our knowledge of the underlying pathways in the pathogenesis of RA. These genes may also serve as diagnostic biomarkers and therapeutic targets for RA; however, further experimental validation is necessary following the bioinformatic analysis to determine our conclusions.

Effects of ginsenoside Rb1 on spinal cord ischemia-reperfusion injury in rats.

BACKGROUND: The aim of this study was to evaluate the effects of different doses of ginsenoside Rb1 (GRb1) pretreatment on spinal cord ischemia-reperfusion (SCII) in rats and explore the potential mechanisms about the expression of survivin protein after the intervention. METHODS: A total of 90 healthy adult Sprague-Dawley (SD) rats were randomly divided into six groups: sham-operated (n = 15), SCII model (n = 15), and GRb1-treated groups (n = 60). The GRb1-treated group was divided into four subgroups: 10 mg/kg, 20 mg/kg, 40 mg/kg, and 80 mg/kg (n = 15). The corresponding dose of GRb1 was injected intraperitoneally 30 min before operation and every day after operation. Forty-eight hours after model establishment, the neurological function of hind limbs was measured with Basso, Beattie, and Bresnahan (BBB) scale. The superoxide dismutase (SOD) and malondialdehyde (MDA) levels in serum and spinal cord tissue were detected respectively. The expression of survivin protein was observed by immunofluorescence staining. HE and TUNEL staining were used to observe neural cell injury and apoptosis, respectively, in the spinal cord of rats with SCII. RESULTS: The intervention of different doses of GRb1 could increase SOD activity and decrease MDA content in serum and spinal cord tissue, increase survivin protein expression, and decrease neuronal apoptosis. It was dose-dependent, but there was no significant change between 40 mg/kg and 80 mg/kg. CONCLUSIONS: GRb1 could reduce the cell apoptosis induced by SCII through inhibiting oxidative stress. It can also inhibit apoptosis by promoting the expression of Survivin protein. Ginsenoside Rb1 had a dose-dependent protective effect on SCII in the dose range of 10 mg/kg-40 mg/kg.