American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Sep 2014
Randomized Controlled Trial Multicenter Study Comparative StudyA Functional Synonymous Coding Variant in the IL1RN Gene Associates with Survival in Septic Shock.
Death from infection is a highly heritable trait, yet there are few genetic variants with known mechanism influencing survival during septic shock. ⋯ In European ancestry subjects, the IL1RN variant rs315952C is preferentially transcribed and associated with increased evoked plasma IL1RA and with improved survival from septic shock. It may be that genetically determined IL1RA levels influence survival from septic shock.
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Am. J. Respir. Crit. Care Med. · Sep 2014
Controlled Clinical TrialEpithelial Interleukin-25 is a Key Mediator in Th2-high, Corticosteroid-responsive Asthma.
Activation of type 2 cytokine pathways plays a central role in a large subset of subjects with asthma. Th2-high and Th2-low asthma have distinct clinical, pathologic, and molecular phenotypes and respond differently to therapy. The factors that initiate type 2 responses in some subjects with asthma are unknown. ⋯ IL-25 measurements identify two subsets of subjects with distinct asthma phenotypes and different responses to ICS. Because IL-25 has a major role in triggering type 2 responses, bronchial epithelial IL-25 expression is likely a key determinant of type 2 response activation in asthma. Plasma IL-25 level reflects airway IL-25/type 2 response activation and may be useful for predicting responses to asthma therapy.
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Am. J. Respir. Crit. Care Med. · Sep 2014
Genome-wide Interrogation of Longitudinal FEV1 in Children with Asthma.
Most genomic studies of lung function have used phenotypic data derived from a single time-point (e.g., presence/absence of disease) without considering the dynamic progression of a chronic disease. ⋯ This study offers a strategy to explore the genetic determinants of longitudinal phenotypes, provide a comprehensive picture of disease pathophysiology, and suggest potential treatment targets.
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It is often difficult to accurately predict when, why, and which patients develop shock, because signs of shock often occur late, once organ injury is already present. Three levels of aggregation of information can be used to aid the bedside clinician in this task: analysis of derived parameters of existing measured physiologic variables using simple bedside calculations (functional hemodynamic monitoring); prior physiologic data of similar subjects during periods of stability and disease to define quantitative metrics of level of severity; and libraries of responses across large and comprehensive collections of records of diverse subjects whose diagnosis, therapies, and course is already known to predict not only disease severity, but also the subsequent behavior of the subject if left untreated or treated with one of the many therapeutic options. ⋯ To address these issues, multivariable models using machine learning data-driven classification techniques can be used to parsimoniously predict cardiorespiratory insufficiency. We briefly describe how these machine learning approaches are presently applied to address earlier identification of cardiorespiratory insufficiency and direct focused, patient-specific management.