A hydrophilic matrix of periodate-oxidized dextran was used as a double-sided linker to covalently immobilize Staphylococcus aureus protein A (SpA) molecules onto a poly-L-lysine-modified piezoelectric crystal surface to improve their stability, activity, and binding specificity with human immunoglobulin G (IgG) in flow injection assays. The prepared sensing crystals displayed best sensitivity and reusability at a flow rate of 140 microL/min. A human IgG concentration as low as 0.3 nM can be detected by this system. Up to 19 successive assay repetitions were achieved without significant loss of sensitivity using the same crystal. The analysis of adsorption kinetics indicates that such a preparation can greatly increase the amount of available active human IgG binding sites on immobilized SpA. Hardly any response arising from unspecific binding was detected. In addition, the sensing crystal prepared by this method was found to retain activity better than one prepared via direct deposition when stored in either wet or dry states. Finally, the prepared SpA-coated crystals were applied to the affinity immobilization of polyclonal goat anti-Schistosoma japonicum glutathione-S-transferase (GST) and were able to subsequently detect GST and its genetically engineered mutant either in a purified form or in the crude cell lysate.

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BackgroundEpilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.MethodologyThis study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.Principal FindingsWe obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.ConclusionWe report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

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