990 resultados para Immune selection


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Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.

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Antigenically evolving pathogens such as influenza viruses are difficult to control owing to their ability to evade host immunity by producing immune escape variants. Experimental studies have repeatedly demonstrated that viral immune escape variants emerge more often from immunized hosts than from naive hosts. This empirical relationship between host immune status and within-host immune escape is not fully understood theoretically, nor has its impact on antigenic evolution at the population level been evaluated. Here, we show that this relationship can be understood as a trade-off between the probability that a new antigenic variant is produced and the level of viraemia it reaches within a host. Scaling up this intra-host level trade-off to a simple population level model, we obtain a distribution for variant persistence times that is consistent with influenza A/H3N2 antigenic variant data. At the within-host level, our results show that target cell limitation, or a functional equivalent, provides a parsimonious explanation for how host immune status drives the generation of immune escape mutants. At the population level, our analysis also offers an alternative explanation for the observed tempo of antigenic evolution, namely that the production rate of immune escape variants is driven by the accumulation of herd immunity. Overall, our results suggest that disease control strategies should be further assessed by considering the impact that increased immunity--through vaccination--has on the production of new antigenic variants.

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The dynamics of a population undergoing selection is a central topic in evolutionary biology. This question is particularly intriguing in the case where selective forces act in opposing directions at two population scales. For example, a fast-replicating virus strain outcompetes slower-replicating strains at the within-host scale. However, if the fast-replicating strain causes host morbidity and is less frequently transmitted, it can be outcompeted by slower-replicating strains at the between-host scale. Here we consider a stochastic ball-and-urn process which models this type of phenomenon. We prove the weak convergence of this process under two natural scalings. The first scaling leads to a deterministic nonlinear integro-partial differential equation on the interval $[0,1]$ with dependence on a single parameter, $\lambda$. We show that the fixed points of this differential equation are Beta distributions and that their stability depends on $\lambda$ and the behavior of the initial data around $1$. The second scaling leads to a measure-valued Fleming-Viot process, an infinite dimensional stochastic process that is frequently associated with a population genetics.

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© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

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The process of determining the level of care and specific postacute care facility for stroke patients has not been adequately studied. The objective of this study was to better understand the factors that influence postacute care decisions by surveying stroke discharge planners. Requests were sent to discharge planners at 471 hospitals in the Northeast United States to complete an online survey regarding the factors impacting the selection of postacute care. Seventy-seven (16%) discharge planners completed the online survey. Respondents were mainly nurses and social workers and 73% reported ≥20 years healthcare experience. Patients and families were found to be significantly more influential than physicians (P < 0.001) and other clinicians (P = 0.04) in influencing postdischarge care. Other clinicians were significantly more influential than physicians (P < 0.001). Insurance and quality of postacute care were the factors likely to most affect the selection of postacute care facility. Insurance was also identified as the greatest barrier in the selection of level of postacute care (70%; P < 0.001) and specific postacute care facility (46%; P = 0.02). More than half reported that pressure to discharge patients quickly impacts a patients' final destination. Nonclinical factors are perceived by discharge planners to have a major influence on postacute stroke care decision making.

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info:eu-repo/semantics/nonPublished

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info:eu-repo/semantics/nonPublished