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The human immune system is studied at different scales (molecular, cellular, tissue, organism, population) with varied granularity and spatiotemporal dimensions. Innate and adaptive immune responses to viral infections are researched in vitro, in vivo, and in silico using mathematical and computational methods. The construction of computational models is an iterative and interactive process which combines existing knowledge and hypotheses (knowledge-driven approach) with empirical datasets (data-driven approach). The resulting models should ideally provide reusability, composability, and interoperability to support system simulation. The main premises of this paper are (1) computational models can be used not only for representation of existing knowledge, but also as experimental tools to demonstrate, exemplify, create, and serve as test-beds for the analysis of empirical data; and (2) computational models should be evaluated in terms of their usability (defined as a measure of how well a specified group of users can achieve predefined goals effectively, efficiently, and satisfactorily in a specified context of use). The usability measure is specified by the context information: user characteristics, their goals, and additional constraints (e.g., data availability, temporal limitations). This paper focuses on the modelling process performed by multidisciplinary teams and the usability of models for the exploration of immune response to viral infections. [ABSTRACT FROM AUTHOR]

Currently, most classification algorithms may suffer from serious misclassification when faced with unbalanced or new data, for lacking feedback and update capability. Many existing classification methods learn from the continuous learning mechanism in the biological immune system to realize the learning of new data. However, these methods in classifying new data, because of the lack of feedback, lead to a search for a long time, even may deviate from the correct results. Humans can make the immune response proceed to a certain extent as they wish through a variety of intervention techniques. Motivated by this, this paper proposes a continual learning classification method with human-in-the-loop (H-CLCM) based on the artificial immune system. H-CLCM adjusts parameters online corresponding to misidentified data by integrating human experience during the testing phase. This enables it not only to converge to an accurate prediction model at minimal cost but also to have the capability to learn new classes of data without retraining the classifier. Experiments on 10 benchmark datasets demonstrate the performances and advantages of the proposed method. The experimental results show that H-CLCM has superior classification performance than the other approaches under the same data. • H-CLCM uses human intervention to improve its performance in real time. • H-CLCM adjusts cell parameters of error detection or increase the number of cells. • H-CLCM improves performance in identifying new types of data. [ABSTRACT FROM AUTHOR]

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Atherosclerosis (AS) is a chronic inflammatory disease caused by disorders of lipid metabolism. Abnormal deposition of low-density lipoproteins in the arterial wall stimulates the activation of immune cells, including the adhesion and infiltration of monocytes, the proliferation and differentiation of macrophages and lymphocytes, and the activation of their functions. The complex interplay between immune cells coordinates the balance between pro- and anti-inflammation and plays a key role in the progression of AS. Therefore, targeting immune cell activity may lead to the development of more selective drugs with fewer side effects to treat AS without compromising host defense mechanisms. At present, an increasing number of studies have found that the active ingredients of traditional Chinese medicine (TCM) can regulate the function of immune cells in multiple ways to against AS, showing great potential for the treatment of AS and promising clinical applications. In this paper, we review the mechanisms of immune cell action in AS lesions and the potential targets and/or pathways for immune cell regulation by the active ingredients of TCM to promote the understanding of the immune system interactions of AS and provide a relevant basis for the use of active ingredients of TCM as natural adjuvants for AS immunotherapy. [Display omitted] ● The immune system plays an important role in the progression of atherosclerosis. ● The active ingredients of TCM regulate the state and function of immune cells. ● The active ingredients of TCM can against atherosclerosis through immune pathways. ● TCMs are promising immunotherapy adjuvants for anti-atherosclerosis. [ABSTRACT FROM AUTHOR]

The bursa of Fabricius, the acknowledged central humoral immune organ, plays a vital role in B lymphocyte differentiation. However, there are few reports of the molecular basis of the mechanism on immune induction and potential antitumor activity of bursal-derived peptides. In this paper, a novel bursal-derived pentapeptide-II (BPP-II, MTLTG) was isolated and exerted immunomodulatory functions on antibody responses in vitro. Gene microarray analyses demonstrated that BPP-II regulated expression of 2478 genes in a mouse-derived hybridoma cell line. Immune-related gene ontology functional procedures were employed for further functional analysis. Furthermore, the majority of BPP-II-regulated pathways were associated with immune responses and tumor processes. Moreover, BPP-II exhibited immunomodulatory effects on antigen-specific immune responses in vivo, including enhancement of avian influenza virus (H9N2 subtype)-specific antibody and cytokine production and modification of T cell immunophenotypes and lymphocyte proliferation. Finally, BPP-II triggered p53 expression and stabilization and selectively inhibited tumor cell proliferation. These data identified the multifunctional factor, BPP-II, as a novel biomaterial representing an important linking between the humoral central immune system and immune induction, including antitumor. Information generated in this study elucidates further the mechanisms involved in humoral immune system and represents the potential basis of effective immunotherapeutic strategies for treating human tumors and immune improvement. [ABSTRACT FROM AUTHOR]

All nutrients play a role in maintaining the immune system and providing substrate for the response. γ;δ T cells, on the other hand, seem to have a unique response to certain dietary bioactive components found in the plant family. Although the identification of those components is not well known yet, members of the proanthocyanidin family and the anthocyanin family of compounds are candidates. Because grapes and grape products contain both of these types of compounds, I hypothesized that grapes may help maintain or support the immune response, specifically the γδ T cell. Data from intact animal studies show that immune function is supported by grape products. In humans, relatively little research has been conducted using the food as an intervention; however, a study currently in progress showed that Concord grape juice supported circulating γδ T cells and maintained immune function, whereas participants receiving the placebo juice had changes associated with reduced immunity. After an overview of immunity, this paper will focus on reviewing the literature on grapes and other food products made from grapes and their potential for interaction with the γδ T cell in whole-body systems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose. [Copyright &y& Elsevier]

Abstract: Consider the following stochastic model for immune response. Each pathogen gives birth to a new pathogen at rate . When a new pathogen is born, it has the same type as its parent with probability . With probability , a mutation occurs, and the new pathogen has a different type from all previously observed pathogens. When a new type appears in the population, it survives for an exponential amount of time with mean 1, independently of all the other types. All pathogens of that type are killed simultaneously. Schinazi and Schweinsberg [R.B. Schinazi, J. Schweinsberg, Spatial and non-spatial stochastic models for immune response, Markov Process. Related Fields (2006) (in press)] have shown that this model on behaves rather differently from its non-spatial version. In this paper, we show that this model on a homogeneous tree captures features from both the non-spatial version and the version. We also obtain comparison results, between this model and the basic contact process on general graphs. [Copyright &y& Elsevier]