Oligoclonal IgG antibodies in multiple sclerosis target patient-specific peptides.

IgG oligoclonal bands (OCBs) are present in the cerebrospinal fluid (CSF) of more than 95% of patients with multiple sclerosis (MS), and are considered to be the immunological hallmark of disease. However, the target specificities of the IgG in MS OCBs have remained undiscovered. Nevertheless, evidence that OCBs are associated with increased levels of disease activity and disability support their probable pathological role in MS. We investigated the antigen specificity of individual MS CSF IgG from 20 OCB-positive patients and identified 40 unique peptides by panning phage-displayed random peptide libraries. Utilizing our unique techniques of phage-mediated real-time Immuno-PCR and phage-probed isoelectric focusing immunoblots, we demonstrated that these peptides were targeted by intrathecal oligoclonal IgG antibodies of IgG1 and IgG3 subclasses. In addition, we showed that these peptides represent epitopes sharing sequence homologies with proteins of viral origin, and proteins involved in cell stress, apoptosis, and inflammatory processes. Although homologous peptides were found within individual patients, no shared peptide sequences were found among any of the 42 MS and 13 inflammatory CSF control specimens. The distinct sets of oligoclonal IgG-reactive peptides identified by individual MS CSF suggest that the elevated intrathecal antibodies may target patient-specific antigens.

The "Smart Dining Table": Automatic Behavioral Tracking of a Meal with a Multi-Touch-Computer.

Studying how humans eat in the context of a meal is important to understanding basic mechanisms of food intake regulation and can help develop new interventions for the promotion of healthy eating and prevention of obesity and eating disorders. While there are a number of methodologies available for behavioral evaluation of a meal, there is a need for new tools that can simplify data collection through automatic and online analysis. Also, there are currently no methods that leverage technology to add a dimension of interactivity to the meal table. In this study, we examined the feasibility of a new technology for automatic detection and classification of bites during a laboratory meal. We used a SUR40 multi-touch tabletop computer, powered by an infrared camera behind the screen. Tags were attached to three plates, allowing their positions to be tracked, and the saturation (a measure of the infrared intensity) in the surrounding region was measured. A Kinect camera was used to record the meals for manual verification and provide gesture detection for when the bites were taken. Bite detections triggered classification of the source plate by the SUR40 based on saturation flux in the preceding time window. Five healthy subjects (aged 20-40 years, one female) were tested, providing a total sample of 320 bites. Sensitivity, defined as the number of correctly detected bites out of the number of actual bites, was 67.5%. Classification accuracy, defined as the number of correctly classified bites out of those detected, was 82.4%. Due to the poor sensitivity, a second experiment was designed using a single plate and a Myo armband containing a nine-axis accelerometer as an alternative method for bite detection. The same subjects were tested (sample: 195 bites). Using a simple threshold on the pitch reading of the magnetometer, the Myo data achieved 86.1% sensitivity vs. 60.5% with the Kinect. Further, the precision of positive predictive value was 72.1% for the Myo vs. 42.8% for the Kinect. We conclude that the SUR40 + Myo combination is feasible for automatic detection and classification of bites with adequate accuracy for a range of applications.