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In this report, cerium doped-ferrite nanoparticles (Ce–Fe3O4 NPs), a novel ceria nanostructure, were proposed to have intrinsic peroxidase-like activity toward a classic peroxidase substrate in the presence of H2O2.

We developed a new biosensor for the detection of aflatoxin B1(AFB1) based on the interaction of gold nanoparticles (AuNPs) with the aptamer. Aggregation of AuNPs was induced by desorption of the AFB1 binding aptamer from the surface of AuNPs as a result of the aptamer target interaction leading to the color change of AuNPs from red to purple. The linear range of the colorimetric aptasensor covered a large variation of AFB1 concentrations from 80 to 270 nM and the detection limit of 7 nM was obtained. Also, the catalytic activity of the aggregated AuNPs greatly enhanced the chemiluminescence (CL) reaction, where the detection limit was determined at 0.5 nM with a regression coefficient of R 2 = 0.9921. We have also shown that the sensitivity of detection was increased by employing CL and using the catalytic activity of aggregated AuNPs, during luminol-hydrogen peroxide reaction. Therefore the proposed nanobiosensor was demonstrated to be sensitive, selective, and simple, introducing a viable alternative for rapid screening of toxin in foods.

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Background Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed. Results In this study, by using an up-to-date dataset, a machine learning model has been trained successfully to predict the toxicity of antimicrobial peptides. The comprehensive set of features of both physico-chemical and linguistic-based with local and global essences have undergone feature selection to identify key properties behind toxicity of antimicrobial peptides. After feature selection, the hybrid model showed the best performance with a recall of 0. 876 and a F1 score of 0. 849. Conclusions The obtained model can be useful in extracting AMPs with low toxicity from AMP libraries in clinical applications. On the other hand, several properties with local nature including positions of strand forming and hydrophobic residues in final selected features show that these properties are critical definer of peptide properties and should be considered in developing models for activity prediction of peptides. The executable code is available at https://git.io/JRZaT.