Patterns of de novo allo B cells and antibody formation in chronic cardiac allograft rejection after alemtuzumab treatment.

Even though the etiology of chronic rejection (CR) is multifactorial, donor specific antibody (DSA) is considered to have a causal effect on CR development. Currently the antibody-mediated mechanisms during CR are poorly understood due to lack of proper animal models and tools. In a clinical setting, we previously demonstrated that induction therapy by lymphocyte depletion, using alemtuzumab (anti-human CD52), is associated with an increased incidence of serum alloantibody, C4d deposition and antibody-mediated rejection in human patients. In this study, the effects of T cell depletion in the development of antibody-mediated rejection were examined using human CD52 transgenic (CD52Tg) mice treated with alemtuzumab. Fully mismatched cardiac allografts were transplanted into alemtuzumab treated CD52Tg mice and showed no acute rejection while untreated recipients acutely rejected their grafts. However, approximately half of long-term recipients showed increased degree of vasculopathy, fibrosis and perivascular C3d depositions at posttransplant day 100. The development of CR correlated with DSA and C3d deposition in the graft. Using novel tracking tools to monitor donor-specific B cells, alloreactive B cells were shown to increase in accordance with DSA detection. The current animal model could provide a means of testing strategies to understand mechanisms and developing therapeutic approaches to prevent chronic rejection.

Synaptic plasticity in spiking neural networks (SP(2)INN): a system approach.

In this paper, we present a digital system called (SP/sup 2/INN) for simulating very large-scale spiking neural networks (VLSNNs) comprising, e.g., 1000000 neurons with several million connections in total. SP/sup 2/INN makes it possible to simulate VLSNN with features such as synaptic short term plasticity, long term plasticity as well as configurable connections. For such VLSNN the computation of the connectivity including the synapses is the main challenging task besides computing the neuron model. We describe the configurable neuron model of SP/sup 2/INN, before we focus on the computation of the connectivity. Within SP/sup 2/INN, connectivity parameters are stored in an external memory, while the actual connections are computed online based on defined connectivity rules. The communication between the SP/sup 2/INN processor and the external memory represents a bottle-neck for the system performance. We show this problem is handled efficiently by introducing a tag scheme and a target-oriented addressing method. The SP/sup 2/INN processor is described in a high-level hardware description language. We present its implementation in a 0.35 /spl mu/m CMOS technology, but also discuss advantages and drawbacks of implementing it on a field programmable gate array.