Lin, Mingkai

Spinal cord stimulation (SCS), first used for pain relief in 1967, serves as an invasive neuromodulation with the targeted delivery of stimulations and the alertness of neural activities. Based on the gate-controlled theory of pain and multiple research studies, the application of SCS to motor recovery, pain relief, and sensory and cognitive restoration has been shown to be significant. Considering that movement disorders and pain are complications of various diseases, the categories, applications, and technical operation of SCS are coming of age. In terms of its impact on the immune system, SCS alters the trajectory of neurological diseases by modulating local immune responses through immunomodulatory mechanisms that vary depending on the different patterns of SCS. In addition to existing targets, the combinations of SCS and emerging techniques deepen the research at the molecular level, contributing to the exploration of potential targets and novel treatments. However, to secure a place in the future trend of non-invasive and precise treatment, it is necessary to improve SCS in terms of mechanism research, cost reduction, and application to more diseases.

IntroductionAlzheimer's disease (AD) is a neurodegenerative disease with neurogenic fiber tangles caused by amyloid-β protein plaques and tau protein hyperphosphorylation as the pathological manifestations. This study was based on multi-omics to investigate the mechanisms and immune characterization of AD.Material and methodsBased on bulk RNA-seq (GSE122063 and GSE97760), we screened potential biomarkers for AD by differential expression analysis and machine learning algorithms. Then, we elaborated the expression characteristics and immune functions of the above biomarkers by scRNA-seq (single-cell RNA sequencing) data analysis (GSM4996463 and GSM4996462) and immune infiltration analysis.ResultsFive biomarkers (RBM3, GOLGA8A, ALS2, FSD2, and LOC100287628) were identified using machine learning algorithms. Single-cell analysis revealed distinct expression patterns of these biomarkers in astrocytes from AD samples compared to normal samples. Additionally, three key biomarkers were selected based on interaction networks, and the diagnostic models indicated high diagnostic efficacy for these biomarkers. Based on immune infiltration and correlation analyses, RBM3, GOLGA8A, and ALS2 were all highly correlated with CD8 T cell content in the immune microenvironment of AD.ConclusionsThe biomarkers identified in this study demonstrate significant diagnostic potential for AD. Notably, the downregulation of RBM3 in astrocytes and the decreased presence of CD8 T cells infiltrating brain tissue are potential risk factors for AD.