Rapid Biosensing Method for Detecting Protein-DNA Interactions

Immuno-Rolling Circle Amplification (Immuno-RCA): Biosensing Strategies, Practical Applications, and Future Perspectives

In the rapidly evolving field of life sciences and biomedicine, detecting low-abundance biomolecules, and ultraweak biosignals presents significant challenges. This has spurred a rapid development of analytical techniques aiming for increased sensitivity and specificity. These advancements, including signal amplification strategies and the integration of biorecognition events, mark a transformative era in bioanalytical precision and accuracy. A prominent method among these innovations is immuno-rolling circle amplification (immuno-RCA) technology, which effectively combines immunoassays with signal amplification via RCA. This process starts when a targeted biomolecule, such as a protein or cell, binds to an immobilized antibody or probe on a substrate. The introduction of a circular DNA template triggers RCA, leading to exponential amplification and significantly enhanced signal intensity, thus the target molecule is detectable and quantifiable even at the single-molecule level. This review provides an overview of the biosensing strategy and extensive practical applications of immuno-RCA in detecting biomarkers. Furthermore, it scrutinizes the limitations inherent to these sensors and sets forth expectations for their future trajectory. This review serves as a valuable reference for advancing immuno-RCA in various domains, such as diagnostics, biomarker discovery, and molecular imaging.

GMean-a semi-supervised GRU and K-mean model for predicting the TF binding site

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

Novel Approaches to Enzyme-Based Electrochemical Nanobiosensors

Electrochemistry is a genuinely interdisciplinary science that may be used in various physical, chemical, and biological domains. Moreover, using biosensors to quantify biological or biochemical processes is critical in medical, biological, and biotechnological applications. Nowadays, there are several electrochemical biosensors for various healthcare applications, such as for the determination of glucose, lactate, catecholamines, nucleic acid, uric acid, and so on. Enzyme-based analytical techniques rely on detecting the co-substrate or, more precisely, the products of a catalyzed reaction. The glucose oxidase enzyme is generally used in enzyme-based biosensors to measure glucose in tears, blood, etc. Moreover, among all nanomaterials, carbon-based nanomaterials have generally been utilized thanks to the unique properties of carbon. The sensitivity can be up to pM levels using enzyme-based nanobiosensor, and these sensors are very selective, as all enzymes are specific for their substrates. Furthermore, enzyme-based biosensors frequently have fast reaction times, allowing for real-time monitoring and analyses. These biosensors, however, have several drawbacks. Changes in temperature, pH, and other environmental factors can influence the stability and activity of the enzymes, affecting the reliability and repeatability of the readings. Additionally, the cost of the enzymes and their immobilization onto appropriate transducer surfaces might be prohibitively expensive, impeding the large-scale commercialization and widespread use of biosensors. This review discusses the design, detection, and immobilization techniques for enzyme-based electrochemical nanobiosensors, and recent applications in enzyme-based electrochemical studies are evaluated and tabulated.

A waste-free entropy-driven DNA nanomachine for smartly designed photoelectrochemical biosensing of MicroRNA-155

DNA nanotechnology has been booming in many fields such as biosensors, logic gates, and material science. Typically, as a kind of powerful isothermal and enzyme-free DNA amplifier in biosensors, entropy-driven DNA nanomachines are superior to hairpin-based ones in speed, specificity, stability, and simplicity. However, the atomic economy of non-covalent molecular reactions in these machines is not high, and DNAs waste is typically generated during operation. Herein, in order to further save costs and improve the performance, we report a novel design for a smart photoelectrochemical (PEC) biosensor of microRNA-155 by engineering waste-free entropy-driven DNA amplifiers conjugated to superparamagnetic Fe₃O₄@SiO₂ particles. This elegant design efficiently avoids leaving redundant DNA strands and waste complex in the amplification system, and all the displaced DNA strands can be regenerated into double-stranded structures, making the reaction irreversible. Thanks to superparamagnetic Fe₃O₄@SiO₂ particles, this strategy is achieved by effectively enriching, extracting, and cleaning target analogs to prevent co-existing species from remaining on the modified electrode surface, enabling a highly specific and sensitive PEC biosensor. This innovative study will be a new perspective on microRNAs detection in complex biological systems, paving the way for the design of waste-free DNA molecular machines and promoting the development of DNA nanotechnology.

Recent Advances in Rapid and Highly Sensitive Detection of Proteins and Specific DNA Sequences Using a Magnetic Modulation Biosensing System

In early disease stages, biomolecules of interest exist in very low concentrations, presenting a significant challenge for analytical devices and methods. Here, we provide a comprehensive overview of an innovative optical biosensing technology, termed magnetic modulation biosensing (MMB), its biomedical applications, and its ongoing development. In MMB, magnetic beads are attached to fluorescently labeled target molecules. A controlled magnetic force aggregates the magnetic beads and transports them in and out of an excitation laser beam, generating a periodic fluorescent signal that is detected and demodulated. MMB applications include rapid and highly sensitive detection of specific nucleic acid sequences, antibodies, proteins, and protein interactions. Compared with other established analytical methodologies, MMB provides improved sensitivity, shorter processing time, and simpler protocols.