Machine learning enables non-Gaussian investigation of changes to peripheral nerves related to electrical stimulation.

Electrical stimulation of the peripheral nervous system (PNS) is becoming increasingly important for the therapeutic treatment of numerous disorders. Thus, as peripheral nerves are increasingly the target of electrical stimulation, it is critical to determine how, and when, electrical stimulation results in anatomical changes in neural tissue. We introduce here a convolutional neural network and support vector machines for cell segmentation and analysis of histological samples of the sciatic nerve of rats stimulated with varying current intensities. We describe the methodologies and present results that highlight the validity of the approach: machine learning enabled highly efficient nerve measurement collection, while multivariate analysis revealed notable changes to nerves' anatomy, even when subjected to levels of stimulation thought to be safe according to the Shannon current limits.

Study of the Interactions of Fusarium virguliforme Toxin FvTox1 with Synthetic Peptides by Molecular Simulations and a Label-Free Biosensor.

Fusarium virguliforme is a soil borne pathogen that causes sudden death syndrome (SDS) in soybean plants. This pathogenic disease may result in severe soybean yield suppression and can cause serious economic harm. It has been shown that the FvTox1 toxin produced by the pathogen may be the root cause of foliar SDS. Anti-FvTox1 single-chain variable fragment antibody expressed in transgenic soybean plants was shown to neutralize the FvTox1 toxin involved in foliar SDS development. Here, we have investigated the binding affinities of FvTox1 with four FvTox1-interacting peptides of 7 to 12 amino acids identified from phage display libraries using both bioinformatics-based molecular simulations and label-free bioassays with a unique photonic crystal biosensor. Results from the molecular simulations have predicted the interaction energies and 3-dimensional (3D) structures of FvTox1 and FvTox1-interacting peptide complexes. Our label-free binding assays have further provided the interaction strength of FvTox1 with four different FvTox1-interacting peptides and experimentally confirmed the simulation results obtained from bioinformatics-based molecular calculations.

Label-free detection of cardiac troponin I with a photonic crystal biosensor.

A biosensor has been developed with a photonic crystal structure used in a total-internal-reflection (PC-TIR) configuration for label-free detection of a cardiac biomarker: Troponin I (cTnI). In contrast to a conventional optical microcavity that has a closed structure with its cavity layer sandwiched between two high-reflection surfaces, the PC-TIR configuration creates a unique open microcavity, which allows its cavity layer (sensing layer) to be easily functionalized and directly exposed to analyte molecules for bioassays. In this study, a PC-TIR sensor has been used for the label-free measurements of cardiac biomarkers by monitoring the changes in the resonant condition of the cavity due to biomolecular binding processes. Antibodies against cTnI are immobilized on the sensor surface for specific detection of cTnI with a wide range of concentrations. Detection limit of cTnI with a concentration as low as 0.1ngmL(-1) has been achieved.