While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

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The mutual conversion between light and electricity lies at the heart of optoelectronic and photonic applications. Maximization of the photoelectric conversion is a long‐term goal that can be pursued via the fabrication of devices with ad‐hoc architectures. In this framework, it is of utter importance to harvest and transform light irradiation into high electric potential in specific area for driving functional dielectrics that respond to pure electric field. Here, a nano‐fabrication technology has been devised featuring double self‐alignment that is applied to construct zebra‐like asymmetric heterojunction arrays. Such nanostructured composite, which covers a surface area of 5 × 4 mm2 and contains 500 periodic repeating units, is capable of photo generating voltages as high as 140 V on a flexible substrate. This approach represents a leap over the traditional functionalization process based on simply embedding materials into devices by demonstrating the disruptive potential of integrating oriented nanoscale device components into meta‐material.