Medical Research Council

About: Medical Research Council is a government organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Malaria. The organization has 16430 authors who have published 19150 publications receiving 1475494 citations.

Innate lymphoid cells mediate influenza-induced airway hyper-reactivity independently of adaptive immunity

Abstract: Patients with asthma, a major public health problem, are at high risk for serious disease from influenza virus infection, but the pathogenic mechanisms by which influenza A causes airway disease and asthma are not fully known. We show here in a mouse model that influenza infection acutely induced airway hyper-reactivity (AHR), a cardinal feature of asthma, independently of T helper type 2 (T(H)2) cells and adaptive immunity. Instead, influenza infection induced AHR through a previously unknown pathway that required the interleukin 13 (IL-13)-IL-33 axis and cells of the non-T cell, non-B cell innate lymphoid type called 'natural helper cells'. Infection with influenza A virus, which activates the NLRP3 inflammasome, resulted in much more production of IL-33 by alveolar macrophages, which in turn activated natural helper cells producing substantial IL-13.

Structural mechanisms for domain movements in proteins.

Abstract: We survey all the known instances of domain movements in proteins for which there is crystallographic evidence for the movement. We explain these domain movements in terms of the repertoire of low-energy conformation changes that are known to occur in proteins. We first describe the basic elements of this repertoire, hinge and shear motions, and then show how the elements of the repertoire can be combined to produce domain movements. We emphasize that the elements used in particular proteins are determined mainly by the structure of the interfaces between the domains.

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Abstract: Prediction models are increasingly used to complement clinical reasoning and decision making in modern medicine in general, and in the cardiovascular domain in particular. Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in targeted patients. The adoption of such models must guide physician's decision making and an individual's behaviour, and consequently improve individual outcomes and the cost-effectiveness of care. In a series of two articles we review the consecutive steps generally advocated for risk prediction model research. This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers (of whatever type) to existing predictors. Each step is illustrated with empirical examples from the cardiovascular field.