Immunoprotective properties of primary Sertoli cells in mice: potential functional pathways that confer immune privilege.

Primary Sertoli cells isolated from mouse testes survive when transplanted across immunological barriers and protect cotransplanted allogeneic and xenogeneic cells from rejection in rodent models. In contrast, the mouse Sertoli cell line (MSC-1) lacks immunoprotective properties associated with primary Sertoli cells. In this study, enriched primary Sertoli cells or MSC-1 cells were transplanted as allografts into the renal subcapsular area of naive BALB/c mice, and their survival in graft sites was compared. While Sertoli cells were detected within the grafts with 100% graft survival throughout the 20-day study, MSC-1 cells were rejected between 11 and 14 days, with 0% graft survival at 20 days posttransplantation. Nonetheless, the mechanism for primary Sertoli cell survival and immunoprotection remains unresolved. To identify immune factors or functional pathways potentially responsible for immune privilege, gene expression profiles of enriched primary Sertoli cells were compared with those of MSC-1 cells. Microarray analysis identified 2369 genes in enriched primary Sertoli cells that were differentially expressed at ±4-fold or higher levels than in MSC-1 cells. Ontological analyses identified multiple immune pathways, which were used to generate a list of 340 immune-related genes. Three functions were identified in primary Sertoli cells as potentially important for establishing immune privilege: suppression of inflammation by specific cytokines and prostanoid molecules, slowing of leukocyte migration by controlled cell junctions and actin polymerization, and inhibition of complement activation and membrane-associated cell lysis. These results increase our understanding of testicular immune privilege and, in the long-term, could lead to improvements in transplantation success.

Bayesian inference for an adaptive Ordered Probit model: an application to Brain Computer Interfacing.

This paper proposes an algorithm for adaptive, sequential classification in systems with unknown labeling errors, focusing on the biomedical application of Brain Computer Interfacing (BCI). The method is shown to be robust in the presence of label and sensor noise. We focus on the inference and prediction of target labels under a nonlinear and non-Gaussian model. In order to handle missing or erroneous labeling, we model observed labels as a noisy observation of a latent label set with multiple classes (≥ 2). Whilst this paper focuses on the method's application to BCI systems, the algorithm has the potential to be applied to many application domains in which sequential missing labels are to be imputed in the presence of uncertainty. This dynamic classification algorithm combines an Ordered Probit model and an Extended Kalman Filter (EKF). The EKF estimates the parameters of the Ordered Probit model sequentially with time. We test the performance of the classification approach by processing synthetic datasets and real experimental EEG signals with multiple classes (2, 3 and 4 labels) for a Brain Computer Interfacing (BCI) experiment.