On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges.

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG.

Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs.

A quantitative approach to evaluating interictal epileptiform discharges based on interpretable quantitative criteria.

Objective: To provide quantitative measures of the six International Federation of Clinical Neurophysiology (IFCN) criteria for interictal epileptiform discharge (IED) identification and estimate the likelihood of a candidate IED being epileptiform.

Methods: We designed an algorithm to identify five fiducial landmarks (onset, peak, trough, slow-wave peak, offset) of a candidate IED, and from these to quantify the six IFCN features of IEDs. Another model was trained with these features to quantify the probability that the waveform is epileptiform and incorporated into a user-friendly interface.

Towards a streamlined synthesis of peptides containing α,β-dehydroamino acids.

Investigation of a strategy to streamline the synthesis of peptides containing α,β-dehydroamino acids (ΔAAs) is reported. The key step involves generating the alkene moiety elimination of a suitable precursor after it has been inserted into a peptide chain. This process obviates the need to prepare ΔAA-containing azlactone dipeptides to facilitate coupling of these residues.