The potential-resolved strategy has gradually demonstrated its distinct values in electrochemiluminescence (ECL) bio-sensing due to its superior characteristics, such as low instrument requirement, short assay time, and improved sample throughput, in conjunction with spatial-and spectrum-resolved techniques. It has recently been widely generalized into versatile multiple-signal ECL analytic platforms, especially in ratiometric and multiplex ECL sensors, in accordance with some specific principles. Furthermore, luminophore pairs with potential-and wavelength-resolved properties have been utilized to visualize biosensors that display multiple colors depending on analyte concentration. However, only a few comprehensive reports on the principles, construction, and application of various ECL sensors in potential-resolved schemes have been published. This review aims to recount the potential-resolved strategy applying to (a) ratiometric ECL sensors, (b) multiplex ECL sensors, and (c) multicolor ECL sensors and to discuss the distinctions and connections among the application principles of these strategies. Finally, the future prospects of ECL-based potential-resolved analysis are explored.

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Heparanase is the sole endoglucuronidase that degrades heparan sulfate in the cell surface and extracellular matrix (ECM). Several studies have reported the localization of heparanase in the cell nucleus, but the functional role of the nuclear enzyme is still obscure. Subjecting mouse embryonic fibroblasts (MEFs) derived from heparanase knockout (Hpse-KO) mice and applying transposase-accessible chromatin with sequencing (ATAC-seq), we revealed that heparanase is involved in the regulation of chromatin accessibility. Integrating with genome-wide analysis of chromatin states revealed an overall low activity in the enhancer and promoter regions of Hpse-KO MEFs compared with wild-type (WT) MEFs. Western blot analysis of MEFs and tissues derived from Hpse-KO vs. WT mice confirmed reduced expression of H3K27ac (acetylated lysine at N-terminal position 27 of the histone H3 protein). Our results offer a mechanistic explanation for the well-documented attenuation of inflammatory responses and tumor growth in Hpse-KO mice.

Multiple signal strategies remarkably improve the accuracy and efficiency of electrochemiluminescence (ECL) immunoassays, but the lack of potential-resolved luminophore pairs and chemical cross talk hinders their development. In this study, we synthesized a series of gold nanoparticles (AuNPs)/reduced graphene oxide (Au/rGO) composites as adjustable oxygen reduction reaction and oxygen evolution reaction catalysts to promote and modulate tris(2,2′-bipyridine) ruthenium(II) (Ru(bpy) 3 2+ )’s multisignal luminescence. With the increase in the diameter of AuNPs (3 to 30 nm), their ability to promote Ru(bpy) 3 2+ ’s anodic ECL was first impaired and then strengthened, and cathodic ECL was first enhanced and then weakened. Au/rGOs with medium-small and medium-large AuNP diameters remarkably increased Ru(bpy) 3 2+ ’s cathodic and anodic luminescence, respectively. Notably, the stimulation effects of Au/rGOs were superior to those of most existing Ru(bpy) 3 2+ co-reactants. Moreover, we proposed a novel ratiometric immunosensor construction strategy using Ru(bpy) 3 2+ ’s luminescence promoter rather than luminophores as tags of antibodies to achieve signal resolution. This method avoids signal cross talk between luminophores and their respective co-reactants, which achieved a good linear range of 10 −7 to 10 −1 ng/ml and a limit of detection of 0.33 fg/ml for detecting carcinoembryonic antigen. This study addresses the previous scarcity of the macromolecular co-reactants of Ru(bpy) 3 2+ , broadening its application in biomaterial detection. Furthermore, the systematic clarification of the detailed mechanisms for converting the potential-resolved luminescence of Ru(bpy) 3 2+ could facilitate an in-depth understanding of the ECL process and should inspire new designs of Ru(bpy) 3 2+ luminescence enhancers or applications of Au/rGOs to other luminophores. This work removes some impediments to the development of multisignal ECL biodetection systems and provides vitality into their widespread applications.

ABSTRACT- The rise of the internet of things (IoT) and autonomous systems has made connecting vehicles more critical. Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications. With the advent of 5G networks, vehicle-to-everything (V2X) networks are expected to be highly intelligent, reside on superfast, reliable, and low-latency connections. Network slicing, Machine Learning (ML), and Deep Learning (DL) are related to network automation and optimization in V2X communication. Machine Learning and Deep Learning (ML/DL) with network slicing aims to optimize the performance, reliability of the V2X network, personalized services, reduced costs, and scalability and enhance the overall driving experience. These advantages can ultimately lead to a safer and more efficient transportation system. However, existing Long-Term Evolution (LTE) systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels. Machine learning algorithms mitigate complexity levels, which can be highly instrumental in such vehicular communication systems. This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing, a different configuration of slicing, the requirements of slicing, and the ML algorithm used to control and manage to slice. This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases, focusing on V2X network slicing and considering efficient control and management. The enabler technologies are considered in light of the network requirements, particular configurations, and the underlying methods and algorithms, with a review of some critical challenges and possible solutions available. The paper concludes with a future roadmap by discussing some open research issues and future directions.