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

Investigation of a strategy to streamline the synthesis of peptides containing α,ß-dehydroamino acids (ΔAAs) is reported. The key step involves generating the alkene moiety via 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. Z-dehydroaminobutyric acid (Z-ΔAbu) could be constructed most efficiently via EDC/CuCl-mediated dehydration of Thr. Formation of Z-ΔPhe by this or other dehydration methods was unsuccessful. Production of the bulky ΔVal residue could be accomplished by DAST-promoted dehydrations of ß-OHVal or by DBU-triggered eliminations of sulfonium ions derived from penicillamine derivatives. However, competitive formation of an oxazoline byproduct remains problematic.

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. RESULTS: The model's performance is excellent (area under the receiver operating characteristic curve (AUROC) = 0.88; calibration error 0.03) but lower than human experts (receiver operating characteristic (ROC) curve is below experts' operating points) or a deep neural-network model (SpikeNet; AUCROC = 0.97; calibration error 0.04). The six features were all significant (p<0.001), but not equally important when determining potential epileptiform nature of candidate IEDs: waveform asymmetry was the most (coefficient 0.64) and duration the least discriminative (coefficient 0.09). CONCLUSIONS: Our approach quantifies the six IFCN criteria for IED identification and combines them in an easily interpretable, accessible fashion that accurately captures the likelihood that a candidate waveform is epileptiform. SIGNIFICANCE: This model may assist clinical electroencephalographers decide whether candidate waveforms are epileptiform and may assist trainees learn to identify IEDs.

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. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.