Impact of Antecedent Infections on the Antibodies against Gangliosides and Ganglioside Complexes in Guillain-Barré Syndrome: A Correlative Study.

Background and Aims: Guillain-Barré Syndrome (GBS), an immune-mediated neuropathy, is characterized by antibodies against gangliosides/ganglioside complexes (GSCs) of peripheral nerves. Antecedent infections have been reported to induce antibodies that cross-react with the host gangliosides and thereby have a pivotal role in conferring an increased risk for developing GBS. Data pertaining to the impact of various antecedent infections, particularly those prevalent in tropical countries like India on the ganglioside/GSC antibodies is sparse. We aimed at exploring the association between six antecedent infections and the profile of ganglioside/GSC antibodies in GBS. Methods: Patients with GBS (n = 150) and healthy controls (n = 50) were examined for the serum profile of antibodies against GM1, GM2, GD1a, GD1b, GT1b, and GQ1b and their GSCs by ELISA. These antibodies were correlated with immunoreactivities against Campylobacter jejuni, Japanese encephalitis (JE), dengue, influenza, zika, and chikungunya infections. Results: The frequencies of antibodies against six single gangliosides (P < 0.001) and their GSCs (P = 0.039) were significantly higher in patients as compared to controls. Except for GT1b-antibody which was more frequent in axonal GBS, none of the other ganglioside/GSC antibodies correlated with the electrophysiological subtypes of GBS. Antecedent JE infection was significantly associated with increased frequency of antibodies against GD1a, GD1b, GT1b, and GQ1b. Antibodies against GSCs were not influenced by the antecedent infections. Interpretation: This study for the first time shows an association between antecedent JE infection and ganglioside antibodies in GBS. This finding reinforces the determining role of antecedent infections on ganglioside antibody responses and the subsequent immunological processes in GBS.

A case of central nervous system vasculitis presenting as a mass-like lesion.

Vasculitis of the central nervous system presenting as a mass lesion is a relatively uncommon occurrence. Even more uncommon is a vasculitis mimicking a demyelinating lesion. We present here an interesting case of a 15-year-old boy who was found to have a mass-like lesion on neuroimaging involving the left subcortical white matter and deep gray matter. The differential diagnosis for this lesion was primary demyelination versus a glial tumor, the former being more favored over the latter. Biopsy of this lesion however revealed findings compatible with a vasculitis, which was unexpected given the neuroimaging findings. To the authors' knowledge, case reports in the English literature of a vasculitic lesion mimicking demyelination are scarce. This case also serves as a reminder of the diagnostic difficulty that arises in a pediatric patient with an initial presentation of mass-like lesion.

Human-like object tracking and gaze estimation with PKD android.

As the use of robots increases for tasks that require human-robot interactions, it is vital that robots exhibit and understand human-like cues for effective communication. In this paper, we describe the implementation of object tracking capability on Philip K. Dick (PKD) android and a gaze tracking algorithm, both of which further robot capabilities with regard to human communication. PKD's ability to track objects with human-like head postures is achieved with visual feedback from a Kinect system and an eye camera. The goal of object tracking with human-like gestures is twofold : to facilitate better human-robot interactions and to enable PKD as a human gaze emulator for future studies. The gaze tracking system employs a mobile eye tracking system (ETG; SensoMotoric Instruments) and a motion capture system (Cortex; Motion Analysis Corp.) for tracking the head orientations. Objects to be tracked are displayed by a virtual reality system, the Computer Assisted Rehabilitation Environment (CAREN; MotekForce Link). The gaze tracking algorithm converts eye tracking data and head orientations to gaze information facilitating two objectives: to evaluate the performance of the object tracking system for PKD and to use the gaze information to predict the intentions of the user, enabling the robot to understand physical cues by humans.