Model Of Human Immune System Research Articles

Progress in construction of mouse models to investigate the pathogenesis and immune therapy of human hematological malignancy.

Hematological malignancy is a disease arisen by complicate reasons that seriously endangers human health. The research on its pathogenesis and therapies depends on the usage of animal models. Conventional animal model cannot faithfully mirror some characteristics of human features due to the evolutionary divergence, whereas the mouse models hosting human hematological malignancy are more and more applied in basic as well as translational investigations in recent years. According to the construction methods, they can be divided into different types (e.g. cell-derived xenograft (CDX) and patient-derived xenograft model (PDX) model) that have diverse characteristics and application values. In addition, a variety of strategies have been developed to improve human hematological malignant cell engraftment and differentiation in vivo. Moreover, the humanized mouse model with both functional human immune system and autologous human hematological malignancy provides a unique tool for the evaluation of the efficacy of novel immunotherapeutic drugs/approaches. Herein, we first review the evolution of the mouse model of human hematological malignancy; Then, we analyze the characteristics of different types of models and summarize the ways to improve the models; Finally, the way and value of humanized mouse model of human immune system in the immunotherapy of human hematological malignancy are discussed.

An Architecture for Alert Correlation Inspired By a Comprehensive Model of Human Immune System

Alert correlation is the process of analyzing, relating and fusing the alerts generated by one or more Intrusion Detection Systems (IDS) in order to provide a high-level and comprehensive view of the security situation of the system or network. Different approaches, such as rule-based, prerequisites consequences-based, learning-based and similarity-based approach are used in correlation process. In this paper, a new AIS-inspired architecture is presented for alert correlation. Different aspects of human immune system (HIS) are considered to design iCorrelator. Its three-level structure is inspired by three types of responses in human immune system: the innate immune system's response, the adaptive immune system's primary response, and the adaptive immune system's secondary response. iCorrelator also uses the concepts of Danger theory to decrease the computational complexity of the correlation process without considerable accuracy degradation. By considering the importance of signals in Danger theory, a new alert selection policy is introduced. It is named Enhanced Random Directed Time Window (ERDTW) and is used to classify time slots to Relevant (Dangerous) and Irrelevant (Safe) slots based on the context information gathered during previous correlations. iCorrelator is evaluated using the DARPA 2000 dataset and a netForensics honeynet data. Completeness, soundness, false correlation rate and the execution time are investigated. Results show that iCorrelator generates attack graph with an acceptable accuracy that is comparable to the best known solutions. Moreover, inspiring by the Danger theory and using context information, the computational complexity of the correlation process is decreased considerably and makes it more applicable to online correlation.

Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

growth of intelligent intrusion and diverse attack techniques in network systems stimulate computer scientists and mathematical researchers to challenge the dangers of intelligent attacks. In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. Classification techniques using k-nearest neighbor (k-NN) or Gaussian Mixture (GMM) almost have the common sense that they believe the neighboring data. The algorithm tested use KDD Cup dataset and the result shows a significant improvement in detection accuracy and reducing the false alerts.