To generate antigen-specific responses, T cells and antigen presenting cells (APCs) must physically associate with each other and elaborate soluble factors that drive the full differentiation of each cell type. Immediately after T cell activation, CD4 T cells can produce both interferon gamma (IFN-gamma) and interleukin 4 (IL-4) before polarization into distinct T helper subsets. Inhibition of IL-4 during mixed allogeneic lymphocyte culture resulted in a defect in the ability of APCs to generate sufficient costimulatory signals for activation of alloreactive T cells. In vivo, a deficiency in IL-4 production inhibited the activation of alloreactive IL-2-, IL-4- and IFN-gamma-producing CD4 T cells in mice challenged with allogeneic skin grafts, resulting in prolonged skin graft survival. Thus, production of IL-4 by CD4T cells helps activate alloreactive T cells by affecting APC function.

The ability of CD4+ T cells to reject class I mismatched skin allografts remains controversial. In this study, we compare the ability of CD4+ T cells to reject class I disparate skin grafts differing by either a single class I allelic disparity or only 3 amino acids encoded by the H-2K locus. We demonstrate that skin grafts across a full H-2K allelic disparity, but not across a disparity of only three amino acids are efficiently rejected by CD4+ T cells. This observation is consistent with the possibility that peptides derived from allogeneic class I molecules generated through the major histocompatibility complex (MHC) class II antigen processing pathway can be recognized by host CD4 T cells and lead to rejection of class I mismatched skin grafts. The availability of peptides derived from allogeneic MHC class I molecules for presentation by host MHC class II may determine the efficiency of rejection of class I mismatched allografts by CD4+ T cells. Thus, class I mismatched allografts can be rejected by CD4+ T cells provided that host and donor MHC class I molecules are sufficiently disparate to activate CD4+ effectors.

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One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. In this work, we have developed a software platform that is designed to support high-performance 3D medical image processing for a wide range of applications using increasingly available and affordable commodity computing systems: multi-core, clusters, and cloud computing systems. To achieve scalable, high-performance computing, our platform (1) employs size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D image processing algorithms; (2) supports task scheduling for efficient load distribution and balancing; and (3) consists of a layered parallel software libraries that allow a wide range of medical applications to share the same functionalities. We evaluated the performance of our platform by applying it to an electronic cleansing system in virtual colonoscopy, with initial experimental results showing a 10 times performance improvement on an 8-core workstation over the original sequential implementation of the system.