Background:The issue of low back pain (LBP) has received considerable critical attention and has been a worldwide health problem. Intervertebral disc degeneration (IVDD) is always the subject of many classic studies in this field. The mechanistic basis of IVDD is poorly understood and has produced equivocal results.
Methods: Gene expression profiles (GSE34095,GSE147383) of IVDD patients together with control groups were analyzed in order to identify differentially expressed genes (DEGs) in GEO database.GSE23130 and GSE70362 were applied to validate the obtained key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile correlation between overall immune infiltration levels with pfirmann grades and key genes. We also analyzed the upstream targeting miRNAs of key genes (GSE63492).We used single-cell transcriptome sequencing data (GSE160756) to define several cell clusters of nucleus pulposus (NP),annulus fibrosus (AF) and cartilaginous endplate (CEP) of degenerated disc and obtained the distribution of key genes in different cell clusters.
Results: By developing appropriate p-values and logFC values, we obtained a total of 6 DEGs. We validated 3 key genes (LRPPRC, GREM1 and SLC39A4) by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly these 4 key miRNAs (miR-103a-3p,miR-484,miR-665,miR-107)were identified as upstream regulators targeting key genes using miRNet database and external GEO datasets. Finally, we plotted the spatial distribution of key genes in AF, CEP and NP.
Conclusions: Our study offered a new perspective to identify the creadible and effective gene therapy targets in IVDD.
-Nowadays, the harmful blue-green algae blooms on lakes threaten the daily life of millions of people in China. We designed and developed a cyber physical networking system on Lake Tai for the monitoring and cleanup of the water blooms which is at work in Wuxi City, Jiangsu Province. We designed the sensor device and algorithm to monitor the order of severity of algae bloom. A GIS-based management website is built for the end user to monitor the whole system. In this paper, we focus on the agile sensor and actuator control (ASAC) mechanism to dispatch salvaging boats. The location area and the order of severity of a water bloom change rapidly with the climatic, the terrain and the sewage disposal system. The location and the capacity of salvaging boat are also in changing when the system is running. ASAC is designed to generate optimal dispatch plan in the changing environment. This mechanism also balances workloads among the boats and among the algae factories to achieve an overall high working efficiency. Through the tests in the real system, ASAC largely saved human resource and increased the work efficiency in the cleaning up process.
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