Recombinant human PDCD5 (rhPDCD5) protein is protective in a mouse model of multiple sclerosis.

BackgroundIn multiple sclerosis (MS) and its widely used animal model, experimental autoimmune encephalomyelitis (EAE), autoreactive T cells contribute importantly to central nervous system (CNS) tissue damage and disease progression. Promoting apoptosis of autoreactive T cells may help eliminate cells responsible for inflammation and may delay disease progression and decrease the frequency and severity of relapse. Programmed cell death 5 (PDCD5) is a protein known to accelerate apoptosis in response to various stimuli. However, the effects of recombinant human PDCD5 (rhPDCD5) on encephalitogenic T cell-mediated inflammation remain unknown.MethodsWe examined the effects of intraperitoneal injection of rhPDCD5 (10 mg/kg) on EAE both prophylactically (started on day 0 post-EAE induction) and therapeutically (started on the onset of EAE disease at day 8), with both of the treatment paradigms being given every other day until day 25. Repeated measures two-way analysis of variance was used for statistical analysis.ResultsWe showed that the anti-inflammatory effects of rhPDCD5 were due to a decrease in Th1/Th17 cell frequency, accompanied by a reduction of proinflammatory cytokines, including IFN-γ and IL-17A, and were observed in both prophylactic and therapeutic regimens of rhPDCD5 treatment in EAE mice. Moreover, rhPDCD5-induced apoptosis of myelin-reactive CD4+ T cells, along with the upregulation of Bax and downregulation of Bcl-2, and with activated caspase 3.ConclusionsOur data demonstrate that rhPDCD5 ameliorates the autoimmune CNS disease by inhibiting Th1/Th17 differentiation and inducing apoptosis of predominantly pathogenic T cells. This study provides a novel mechanism to explain the effects of rhPDCD5 on neural inflammation. The work represents a translational demonstration that rhPDCD5 has prophylactic and therapeutic properties in a model of multiple sclerosis

PDCD5 (programmed cell death 5)

Programmed Cell Death 5 (PDCD5) was originally identified as an apoptosis-accelerating molecule that was widely expressed and well-conserved in the process of evolution. It has significant homology to the corresponding proteins of species ranging from yeast to mice gene. PDCD5 can accelerate apoptosis in different type of cells in response to different stimuli, and can also induce different types of cell death, including paraptosis-like cell death. In cells undergoing apoptosis, PDCD5 rapidly translocates from the cytoplasm to the nucleus before phosphatidylserine is externalized and genomic DNA undergoes fragmentation. PDCD5 interacts with TIP60 and TP53 and plays an important positive role in TIP60-P53 signaling pathway. PDCD5 also participates in immune regulation through regulating the level of FOXP3 protein and percentage of regulatory T cells. Dysfunction of PDCD5 was associated in many diseases including different tumors, rheumatoid arthritis and presbycusis, etc

EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation

Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation. However, current methods usually suffer from the drawback that it is difficult to balance the computational cost, estimation accuracy, and theoretical support in a unified framework. To alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence (DST) into semi-supervised medical image segmentation, dubbed Evidential Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to infer accurate uncertainty quantification in a single forward pass. Trustworthy pseudo labels on unlabeled data are generated after uncertainty estimation. The recently proposed consistency regularization-based training paradigm is adopted in our framework, which enforces the consistency on the perturbed predictions to enhance the generalization with few labeled data. Experimental results show that EVIL achieves competitive performance in comparison with several state-of-the-art methods on the public dataset

Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection

Both transduction and rejection have emerged as important techniques for defending against adversarial perturbations. A recent work by Tram\`er showed that, in the rejection-only case (no transduction), a strong rejection-solution can be turned into a strong (but computationally inefficient) non-rejection solution. This detector-to-classifier reduction has been mostly applied to give evidence that certain claims of strong selective-model solutions are susceptible, leaving the benefits of rejection unclear. On the other hand, a recent work by Goldwasser et al. showed that rejection combined with transduction can give provable guarantees (for certain problems) that cannot be achieved otherwise. Nevertheless, under recent strong adversarial attacks (GMSA, which has been shown to be much more effective than AutoAttack against transduction), Goldwasser et al.'s work was shown to have low performance in a practical deep-learning setting. In this paper, we take a step towards realizing the promise of transduction+rejection in more realistic scenarios. Theoretically, we show that a novel application of Tram\`er's classifier-to-detector technique in the transductive setting can give significantly improved sample-complexity for robust generalization. While our theoretical construction is computationally inefficient, it guides us to identify an efficient transductive algorithm to learn a selective model. Extensive experiments using state of the art attacks (AutoAttack, GMSA) show that our solutions provide significantly better robust accuracy