The incidence of Alzheimer’s disease (AD) is increasing year by year, which brings great challenges to human health. However, the pathogenesis of AD is still unclear, and it lacks early diagnostic targets. The entorhinal cortex (EC) is a key brain region for the occurrence of AD neurodegeneration, and neuroinflammation plays a significant role in EC degeneration in AD. This study aimed to reveal the close relationship between inflammation-related genes in the EC and AD by detecting key differentially expressed genes (DEGs) via gene function enrichment pathway analysis. GSE4757 and GSE21779 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used for the standardization and differential analysis of DEGs. Then, significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed to predict the potential biological functions of the DEGs. Finally, the significant expressions of identified DEGs were verified, and the therapeutic values were detected by a receiver operating characteristic (ROC) curve. The results showed that eight up-regulated genes (SLC22A2, ITGB2-AS1, NIT1, FGF14-AS2, SEMA3E, PYCARD, PRORY, ADIRF) and two down-regulated genes (AKAIN1, TRMT2B) may have a potential diagnostic value for AD, and participate in inflammatory pathways. The area under curve (AUC) results of the ten genes showed that they had potential diagnostic value for AD. The AUC of PYCARD was 0.95, which had the most significant diagnostic value, and it is involved in inflammatory processes such as the inflammasome complex adaptor protein. The DEGs screened, and subsequent pathway analysis revealed a close relationship between inflammation-related PYCARD and AD, thus providing a new basis for an early diagnostic target for AD.

A triboelectric nanogenerator-based self-powered resonant sensor is proposed and investigated. By integrating an inductor and a microswitch with a triboelectric nanogenerator, a new type triboelectric nanogenerator is obtained, the pulse voltage output is converted to an oscillating signal with a very stable modulated resonant frequency, immune to the cross disturbance of contact-related variation (force, frequency, distance) and environmental variation, such as humidity and temperature. This is utilized for non-destructive defect detection. When the coil inductor scans the surface of a specimen with defects, varying resonant frequencies are obtained for different types of defects, showing excellent consistency between the experimental and simulated results. The results demonstrate the potential of the self-powered TENG-based resonant sensor to be a highly stable and sensitive magnetic sensor for the non-destructive defect detection applications.

Background: The exact pathogenesis of Alzheimer’s disease (AD), a neurodegenerative disorder, remains unclear. Ferroptosis is a form of cell death characterized by intracellular iron accumulation, and has emerged as a potential contributor to the pathological cascade of AD. Therefore, this study aims to identify core genes that may function as reliable biomarkers for AD through an in-depth analysis of the genetic relationship between ferroptosis-related genes and AD. Methods: This study first obtained the gene expression profiles (GSE140831, GSE63060 and GSE63061 expression profiles). The GSE140831 dataset served as the discovery cohort, and the GSE63060 and GSE63061 datasets were used as independent validation cohorts. R language 4.4.1 was used for standardizing and identifying differentially expressed genes (DEGs) in AD patients in all datasets. Secondly, the ferroptosis-related genes were obtained. By integrating the ferroptosis-related genes, ferroptosis-related DEGs (FRDEGs) were detected. Then, the FRDEGs were verified and evaluated, and the biological functions of the core genes were analyzed. Finally, miRNAs interacting with these core FRDEGs were explored. Results: The study identified nine FRDEGs (ACVR1B, BRPF1, G6PD, KLHDC3, LAMP2, MTCH1, P4HB, PTPN6, RBMS1), which are potentially related and may serve as biomarkers for AD. All nine genes demonstrated statistically significant differential expression (up-regulation) in both independent validation cohorts and in the combined analysis (p < 0.05). Although the area under the curve (AUC) values of these nine genes ranged from 0.61 to 0.71, indicating moderate discriminatory power, these findings suggest that they may be involved in pathways related to AD and are worthy of further investigation as potential auxiliary biomarkers. Finally, a network of hub FRDEGs-miRNAs interaction was constructed. There were 11 miRNAs that may regulate these hub FRDEGs simultaneously. Conclusions: This study showed the significant association of the identified FRDEGs with AD. Also, a core ferroptosis-related biomarker network for miRNAs regulation of AD was constructed. The specific regulatory mechanism is worthy of further investigation.

Parkinson's disease (PD) represents a prevalent neurodegenerative disorder of the central nervous system, with an incidence that is on an upward trajectory. The death of dopaminergic neurons in the substantia nigra (SN) pars compacta and pathological changes are the main features of PD pathogenesis. Autophagy emerges as a pivotal cellular mechanism influencing neuronal attrition and the progression of PD. The main goal of this study was to clarify the close relationship between autophagy and the pathophysiology of PD. This study identified the differentially expressed autophagy genes (DEAGs) associated with PD in SN, which may contribute to the discovery of potential biomarkers for PD diagnosis. This study analyzed the GSE20291 and GSE42966 gene expression profiles from the gene expression omnibus (GEO) database. Analysis to identify differentially expressed genes (DEGs) was conducted utilizing the R programming language. From the database dedicated to autophagy-related genes, a compilation of 259 genes associated with autophagy was identified. The genes differentially expressed in PD and those related to autophagy were integrated and analyzed to identify DEAGs. Then, the differential genes of PD and autophagy genes were analyzed, and four genes (AKT2, ARSA, MAPK9, RGS19) were screened out. Finally, the potential value of DEAGs in PD pathology was verified by the receiver operating characteristic (ROC) curve. This study proposed the potential of autophagy-related genes as clinical biomarkers for PD, providing fresh perspectives on the pathogenic development of PD.