Pesticide residue screening using a novel artificial neural network combined with a bioelectric cellular biosensor.

We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minutes. The novel system was developed using the data (time series) of the electrophysiological responses of three different cultured cell lines against three different pesticide groups (carbamates, pyrethroids, and organophosphates). Using the novel system, we were able to classify correctly the presence of the investigated pesticide groups with an overall success rate of 83.6%. Considering that only 70,000-80,000 samples are annually tested in Europe with current conventional technologies (an extremely minor fraction of the actual screening needs), the system reported in the present study could contribute to a screening system milestone for the future landscape in food safety control.

Engineering of the membrane of fibroblast cells with virus-specific antibodies: A novel biosensor tool for virus detection.

A novel concept for the assay of viral antigens is described. The methodological approach is based on a membrane-engineering process involving the electroinsertion of virus-specific antibodies in the membranes of fibroblast cells. As a representative example, Vero fibroblasts were engineered with antibodies against Cucumber mosaic virus (CMV) and used for the construction of an ultra-sensitive miniature cell biosensor system. The attachment of a homologous virus triggered specific changes to the cell membrane potential that were measured by appropriate microelectrodes, according to the principle of the bioelectric recognition assay (BERA). No change in the membrane potential was observed upon cell contact with the heterologous cucumber green mottle mosaic virus (CGMMV). Fluorescence microscopy observations showed that attachment of CMV particles to membrane-engineered cells was associated with membrane hyperpolarization and increased [Ca(2+)](cyt). In an additional field-based application, we were able to detect CMV-infected tobacco plants at an essentially 100% level of accuracy.