No abstract

Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) is considered a novel anti-tumor target comparable to programmed cell death 1 ligand 1(PD-L1). However, little is known about Siglec-15. Our study aims to understand its expression signature, prognosis value, immune infiltration pattern, and biological function using multi-omic bioinformatics from public databases and verify them in lung cancer patients. Integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals showed Siglec-15 was overexpressed across cancers. Genetic and epigenetic alteration analysis was performed using cBioportal and UALCAN, showed Siglec-15 was regulated at the genetic and epigenetic levels. Survival estimated using Kaplan-Meier plotter indicated high Siglec-15 expression correlated with favorable or unfavorable outcomes depending on the different type and subtype of cancer. Components of immune microenvironment were analyzed using CIBERSORT, and the correlation between immune cells and Siglec-15 was found to be distinct across cancer types. Based on Gene Set Enrichment Analysis, Siglec-15 was implicated in pathways involved in immunity, metabolism, cancer, and infectious diseases. Lung cancer patients with positive Siglec-15 expression showed significantly short survival rates in progressionfree survival concomitant with reduced infiltration of CD20 + B, and dendritic cells by immunohistochemistry. Quantitative real-time PCR results indicated the overexpression of Siglec-15 was correlated with activation of the chemokine signaling pathway. In conclusion, Siglec-15 could serve as a vital prognostic biomarker and play an immune-regulatory role in tumors. These results provide us with clues to better understand Siglec-15 from the perspective of bioinformatics and highlight the importance of Siglec-15 in many types of cancer.

No abstract

No abstract

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.