Engineering biological interfaces represents a powerful means to improve the performance of biosensors. Here, we developed a DNA-engineered nanozyme interface for rapid and sensitive detection of dental bacteria. We employed DNA aptamer as both molecular recognition keys and adhesive substrates to functionalize the nanozyme. Utilizing different immobilization strategies and DNA designs, a range of DNA nanoscale biointerfaces were constructed to modulate enzymatic and biological properties of the nanozyme systems. These functional biointerfaces improved the accessibility of bacteria to the nanozyme surface, providing large signal change range at optimal DNA probe density. The DNA-functionalized nanozymes demonstrate a rapid, label-free, and highly sensitive direct colorimetric detection of Streptococcus mutans, with a detection limit of 12 CFU mL–1, as well as excellent discrimination from other dental bacteria. We demonstrate the use of this biological nanointerface for identifying dental bacteria in salivary samples, showing its potential in clinical prevention and diagnosis of dental diseases.

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DNA treatment of metal nanoparticles provides a potent tool for tuning their native properties and constructing advanced materials. However, there have been limited studies on interactions between DNA and nanomaterial-based artificial enzymes (nanozymes) to influence their intrinsic peroxidase-like properties. Here, we present the utilization of DNA as a capping ligand to engineer various bio-nanointerfaces for high-precise and adjustable regulation of catalytic behaviors of nanozymes toward the oxidation of substrates. The treatment of stiff double-stranded DNA only induced a negligible enhancement of the catalytic activity of nanozymes, and both coil-like single-stranded DNA and hairpin DNA-capped nanoparticles produced a medium signal increase. Interestingly, hybridization chain reaction (HCR) product-treated nanoparticles showed the highest peroxidase-like activities among four DNA structures. Furthermore, significant parameters that influence HCR process and the modulation of catalysis, such as the concentration of the hairpin DNA, the ionic strength, and the amount of nanozyme, were also systematically investigated. On the basis of HCR amplification and iron oxide (Fe 3 O 4 ) nanoparticles, we develop a simple, fast, label-free, and sensitive colorimetric strategy for sensing of a Yersinia pestis-relevant DNA sequence with a detection limit as low as 100 pM as well as single nucleotide polymorphism discrimination. These results highlight DNA engineering as a facile strategy to regulate the catalytic activities of nanozymes and understand the interactions between metallic nanoparticles and nucleic acids for biosensing applications.

Purpose The purpose of this paper is introducing the image processing technology used for fabric analysis, which has the advantages of objective, digital and quick response. Design/methodology/approach This paper briefly describes the key process and module of some typical automatic recognition systems for fabric analysis presented by previous researchers; the related methods and algorithms used for the texture and pattern identification are also introduced. Findings Compared with the traditional subjective method, the image processing technology method has been proved to be rapid, accurate and reliable for quality control. Originality/value The future trends and limitations in the field of weave pattern recognition for woven fabrics have been summarized at the end of this paper.

Color texture classification as a part of fabric analysis is significant for textile manufacturing. In this research, a new artificial intelligence method based on a dual-side co-occurrence matrix and a back propagation neural network has been proposed for color texture classification, which could achieve relatively accurate classification results for yarn-dyed woven fabric compared with the traditional co-occurrence matrix for a single-side image. Firstly, a laboratory dual-side imaging system has been established to digitize the upper-side and lower-side images sequentially. Secondly, the dual-side co-occurrence matrix could be generated based on these dual images; four texture features could be extracted for the evaluation of the fabric texture characteristics. Thirdly, a well-trained back propagation neural network was established with the four defined features as the input vectors and the color texture type of yarn-dyed woven fabric as the output vector. The efficiency of two different classification systems based on a dual-side co-occurrence matrix and a single-side co-occurrence matrix has been compared systematically. Our experimental results show that the artificial intelligence system based on a dual-side co-occurrence matrix and back propagation neural network model could achieve a relatively better classification effect, with the high coefficient ratio ( R = 0.9726) when d = 0.