Extremely sensitive and accurate measurements of protein markers for early detection and monitoring of diseases pose a formidable challenge. Herein, we develop a new type of amplified fluorescence polarization (FP) aptasensor based on allostery-triggered cascade strand-displacement amplification (CSDA) and polystyrene nanoparticle (PS NP) enhancement for ultrasensitive detection of proteins. The assay system consists of a fluorescent dye-labeled aptamer hairpin probe and a PS NP-modified DNA duplex (assistant DNA/trigger DNA duplex) probe with a single-stranded part and DNA polymerase. Two probes coexist stably in the absence of target, and the dye exhibits relatively low FP background. Upon recognition and binding with a target protein, the stem of the aptamer hairpin probe is opened, after which the opened hairpin probe hybridizes with the single-stranded part in the PS NP-modified DNA duplex probe and triggers the CSDA reaction through the polymerase-catalyzed recycling of both target protein and trigger DNA. Throughout this CSDA process, numerous massive dyes are assembled onto PS NPs, which results in a substantial FP increase that provides a readout signal for the amplified sensing process. Our newly proposed amplified FP aptasensor enables the quantitative measurement of proteins with the detection limit in attomolar range, which is about 6 orders of magnitude lower than that of traditional homogeneous aptasensors. Moreover, this sensing method also exhibits high specificity for target proteins and can be performed in homogeneous solutions. In addition, the suitability of this method for the quantification of target protein in biological samples has also been shown. Considering these distinct advantages, the proposed sensing method can be expected to provide an ultrasensitive platform for the analysis of various types of target molecules.
Lipase activity and stability in ionic liquids containing N,N-dialkylimidazolium cations and different anions were investigated in alcoholysis reactions.
Internet of Things is widely used in many fields such as industry, medical care, education, and supply chain. With the participation of multi-authorized entities, a large number of dynamic data will be generated in the basic dimension of time. The operations on these data have to be safe and traceable for use in various forensics and decisions. Therefore, the key point of dynamic data security protection is to reject tampering of unauthorized users and to realize the process in evidence and tracing of the dynamic data operation. In order to find a solution to the problem above, an optimization of dynamic data traceability mechanism based on consortium blockchain is proposed in this article. First, a mathematical model for the security of dynamic data storage has been established, followed by analysis on honest behavior motive of individual node decision-making in group game and distributed node cooperation essence in specific industry background. After that, ownership transition function and the architecture of the dynamic data storage system are optimized; quality and growth characteristics of the system under stochastic state model are analyzed. Result shows that the solution can effectively avoid potential attacks such as tampering and faking under approved accession mode. The mechanism has good application value while ensuring the dynamic data storage security.
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