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Recent development and challenges in DNA biosensing technology for the detection of DNA hybridization are reviewed with respect to their abilities to achieve lower detection limit and higher selectivity. Researchers exploit a range of different chemistries for the development of DNA hybridization biosensors, however all the designs take advantage of heterogenous hybridization between the surface-bound DNA (the probe) and the DNA sample (target) in the solution. The detection protocols include using optical, microgravimetry, and electrochemical-based device to transduce DNA hybridization by observing changes in light, mass/frequency, and current/charge, respectively, upon exposure to the sample. The pros and cons of these biosensor designs are discussed with illustrative examples.

miRNA-21 (miR-21) is a potential biomarker for the monitoring of diseases through its expression levels. Simple, portable and sensitive miR-21 detection of is advantageous for health monitoring in Point of Care Testing (POCT). Gold nanoparticles (AuNP) as excellent colorimetric sensors are widely used in the POCT. However, their low sensitivity is a limitation of their clinical use. Herein, we developed an AuNPs-based miR-21 assay with enzyme-assisted amplification reaction to construct the colorimetric platform capable of detecting as low as 0.1nM. In this platform, template ssDNA as a signal molecule could hybridize with ssDNA-modified AuNPs to generate the color reaction. The target miR-21 specifically hybridized to the template ssDNA, which was then cleaved by exonuclease III (Exo III) to release the target miR-21. As a trigger, miR-21 catalyzed the degradation of the template ssDNA to amplify the signal by Exo III. By hybridizing miR-21 and template ssDNA in the presence of Exo III, R-21 induced a significant decrease in the level of template ssDNA to reduce the aggregation of AuNPs. There is a clear color difference in the presence/absence of miR-21 in the assay. In this assay, the optimal concentration of templated ssDNA and Exo III were 100nM and 0.06U/$\mu$L in a 100$\mu$L detection system. The LoD for UV–Vis spectrum and colorimetric reaction were 0.1nM and 0.5nM, respectively. The detection system has good selectivity and can be used to detect miR-21 in the simulated saliva. It has great potential for application in biomedical research as well as in clinical diagnostics.

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges.