The role of inflammatory reactions in xenotransplantation

Since the original description of hyperacute rejection of the kidney, numerous experimental and clinical studies have attempted to define the precise mechanism(s) of this process. While our interpretation of HAR has advanced, one must recognize this phenomenon as multifactorial and the product of a complex immune/inflammatory reaction. The non-specific effector inflammatory cascade that ensues once an antigen-antibody reaction has initiated hyperacute rejection serves as the target for therapeutic intervention by various modalities. Classically, the platelet-coagulation system has been considered to be the most important and has received the greatest attention. These therapeutic modalities have included heparin, aspirin, dextran, citrate, defibrinating agents, cobra and venom factor, induction of thrombocytopenia by antibodies, and various prostaglandins. All these approaches have been either too toxic or the results too inconclusive to be accepted for generalized use. The difficulty in overcoming the inflammatory process in hyperacute rejection and in explaining the discrepancies in the therapeutic results rest not only in the complexity of the biological reactions, but also in their resiliency and redundancy. That is, there are numerous alternative mediator pathways that can back up the same set of functions. Thus, the inhibition of one arm of an inflammatory reaction can easily be replaced by the release of other inflammatory autocoids with complete mediation of the disease process

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio