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We refine and clinically parameterize a mathematical model of the humoral immune response against Shigella, a diarrheal bacteria that infects 80-165 million people and kills an estimated 600,000 people worldwide each year. Using Latin hypercube sampling and Monte Carlo simulations for parameter estimation, we fit our model to human immune data from two Shigella EcSf2a-2 vaccine trials and a rechallenge study in which antibody and B-cell responses against Shigella′s lipopolysaccharide (LPS) and O-membrane proteins (OMP) were recorded. The clinically grounded model is used to mathematically investigate which key immune mechanisms and bacterial targets confer immunity against Shigella and to predict which humoral immune components should be elicited to create a protective vaccine against Shigella. The model offers insight into why the EcSf2a-2 vaccine had low efficacy and demonstrates that at a group level a humoral immune response induced by EcSf2a-2 vaccine or wild-type challenge against Shigella′s LPS or OMP does not appear sufficient for protection. That is, the model predicts an uncontrolled infection of gut epithelial cells that is present across all best-fit model parameterizations when fit to EcSf2a-2 vaccine or wild-type challenge data. Using sensitivity analysis, we explore which model parameter values must be altered to prevent the destructive epithelial invasion by Shigella bacteria and identify four key parameter groups as potential vaccine targets or immune correlates: 1) the rate that Shigella migrates into the lamina propria or epithelium, 2) the rate that memory B cells (BM) differentiate into antibody-secreting cells (ASC), 3) the rate at which antibodies are produced by activated ASC, and 4) the Shigella-specific BM carrying capacity. This paper underscores the need for a multifaceted approach in ongoing efforts to design an effective Shigella vaccine. [ABSTRACT FROM AUTHOR]

Outlier detection has important applications in various data mining domains such as fraud detection, intrusion detection, customers' behavior and employees' performance analysis. Outliers are characterized by being significantly or "interestingly" different from the rest of the data. In this paper, a novel cluster-based outlier detection method is proposed using a humoral-mediated clustering algorithm (HAIS) based on concepts of antibody secretion in natural immune systems. The proposed method finds meaningful clusters as well as outliers simultaneously. This is an iterative approach where only clusters above threshold (larger sized clusters) are carried forward to the next cycle of cluster formation while removing small sized clusters. This paper also demonstrates through experimental results that the mere existence of outliers severely affects the clustering outcome, and removing those outliers can result in better clustering solutions. The feasibility of the method is demonstrated through simulated datasets, current datasets from the literature as well as a real-world doctors' performance evaluation dataset where the task is to identify potentially under-performing doctors. The results indicate that HAIS has capabilities of detecting single point as well as cluster-based outliers. [ABSTRACT FROM AUTHOR]

The worldwide spread of a novel influenza A (H1N1) virus in 2009 showed that influenza remains a significant health threat, even for individuals in the prime of life. This paper focuses on the unusually high young adult mortality observed during the Spanish flu pandemic of 1918. Using historical records from Canada and the U.S., we report a peak of mortality at the exact age of 28 during the pandemic and argue that this increased mortality resulted from an early life exposure to influenza during the previous Russian flu pandemic of 1889–90. We posit that in specific instances, development of immunological memory to an influenza virus strain in early life may lead to a dysregulated immune response to antigenically novel strains encountered in later life, thereby increasing the risk of death. Exposure during critical periods of development could also create holes in the T cell repertoire and impair fetal maturation in general, thereby increasing mortality from infectious diseases later in life. Knowledge of the age-pattern of susceptibility to mortality from influenza could improve crisis management during future influenza pandemics. [ABSTRACT FROM AUTHOR]

This work aims at identifying a set of humoral immunologic parameters that improve prediction of the activation process in HIV patients. Starting from the well-known impact of humoral immunity in HIV infection, there is still a lack of knowledge in defining the role of the modulation of functional activity and titers of serum antibodies from early stage of infection to the development of AIDS. We propose an integrated approach that combines humoral and clinical parameters in defining the host immunity, implementing algorithms associated with virus control. A number of humoral parameters were simultaneously evaluated in a whole range of serum samples from HIV-positive patients. This issue has been afforded accounting for estimation problems typically related to “feasibility” studies where small sample size in each group and large number of parameters are jointly estimated. We used nonparametric statistical procedures to identify biomarkers in our study which included 42 subjects stratified on five different stages of HIV infection, i.e., Elite Controllers (EC), Long Term Non Progressors (LTNP), HAART, AIDS and Acute Infection (AI). The main goal of the paper is to illustrate a novel profiling method for helping to design a further confirmatory study. A set of seventeen different HIV-specific blood humoral factors were analyzed in all subjects, i.e. IgG and IgA to gp120IIIB, to gp120Bal, to whole gp41, to P1 and T20 gp41 epitopes of the MPER-HR2 region, to QARILAV gp41 epitope of the HR1 region and to CCR5; neutralization activity against five different virus strains and ADCC were also evaluated. Patients were selected on the basis of CD4 cell counts, HIV/RNA and clinical status. The Classification and Regression Trees (CART) approach has been used to uncover specific patterns of humoral parameters in different stages of HIV disease. Virus neutralization of primary virus strains and antibodies to gp41 were required to classify patients, suggesting that clinical profiles strongly rely on functional activity against HIV. [ABSTRACT FROM AUTHOR]

Nature over millions of years has found innovative, robust and effective methods through evolution for helping organisms deal with the challenges they face when attempting to survive in hostile and uncertain environments. Two critical natural mechanisms in this evolutionary process are variation and selection, which form the basis of "evolutionary computing" (EC). EC has proved successful when dealing with complex problems, such as classification, clustering and optimization. In recent years, as our knowledge of microbiology has deepened, researchers have turned to micro-level biology for inspiration to help solve complex problems. This paper describes a novel supervised learning algorithm inspired by the humoral mediated response triggered by the adaptive immune system. The proposed algorithm uses core immune system concepts such as memory cells, plasma cells and B-cells as well as parameters and processes inspired by our knowledge of the microbiology of immune systems, such as negative clonal selection and affinity thresholds. In particular, we show how local and global similarity based measures based on affinity threshold can help to avoid over-fitting data. The novelty of the proposed algorithm is discussed in the context of existing immune system-based supervised learning algorithms. The performance of the proposed algorithm is tested on well-known benchmarked real world datasets and the results indicate performance not worse than existing techniques in most cases and improvement over previously reported results in some. The role of memory cells is highlighted as a key feature in AIS-based supervised learning that deserves further exploration and evaluation. [ABSTRACT FROM AUTHOR]

Since its outbreak in December 2019, the coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to an enormous rise in scientific response with an excess of COVID-19-related studies on the pathogenesis and potential therapeutic approaches. Solid organ transplant (SOT) recipients are a heterogeneous population with long-lasting immunosuppression as a joining element. Immunocompromised patients are a vulnerable population with a high risk of severe infections and an increased infection-related mortality rate. It was postulated that the hyperinflammatory state due to cytokine release syndrome during severe COVID-19 could be alleviated by immunosuppressive therapy in SOT patients. On the other hand, it was previously established that T cell-mediated immunity, which is significantly weakened in SOT recipients, is the main component of antiviral immune responses. In this paper, we present the current state of science on COVID-19 immunology in relation to solid organ transplantation with prospective therapeutic and vaccination strategies in this population. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to determine the effect of ivermectin administration on cell mediated (CMI) and humoral immunity (HI) of rabbits. CMI against dinitrochlorobenzene (DNCB) and sheep red blood cells (SRBC) in rabbits was determined by delayed-type hypersensitivity and macrophage engulfment assay (MEA), respectively; whereas, HI to Pasteurella multocida B2 vaccine and SRBC was determined by indirect haemagglutination assay (IHA) and Jerne hemolytic plaque formation assay (JHPFA), respectively. The rabbits were divided into four major groups (A through D) each subdivided into four sub-groups (1 through 4). Rabbits of group A served as vehicle control while those of groups B, C and D were treated with ivermectin at the dose rates of 200 μg/kg, 400 μg/kg and 600 μg/kg b.w., respectively. Cellular immunity was determined in sub-groups 1 and 2 through DNCB and MEA, respectively while HI was determined in sub-groups 3 and 4 through IHA and JHPFA, respectively. The skin sensitivity to DNCB at 24 and 48 h and macrophage engulfment of SRBC were highest (P >0.05) in rabbits administered with 600 μg/kg b.w. The highest geometric mean titers (14.00±0.31) and number of plaque forming units (1860±0.75) were found in rabbits that received ivermectin at a dose of 600 μg/kg b.w. followed, in order by the groups that received 400 μg/kg, 200 μg/kg b.w. and controls. Leukocyte counts were significantly higher in ivermectin-treated groups (C and D) than group A (vehicle control) and B (ivermectin at the rate of 200 μg/kg). A graded dose immune response suggested an immunopotentiating effect of ivermectin at higher doses. [Copyright &y& Elsevier]