Differential maturation trajectories of innate antiviral immunity in health and atopy.

BACKGROUND: The maturation of innate immune responses in health and atopy is still incompletely understood. METHODS: We aimed to evaluate age-related trajectories of the TLR3 and TLR7/8 pathways from birth to adulthood and whether these differ between healthy and atopic individuals. Peripheral blood mononuclear cells (PBMCs) were isolated from 39 otherwise healthy, atopic and 39 non-atopic subjects, aged 0-45 years. Selected cytokines involved in antiviral responses were measured by Luminex in culture supernatants of poly(I:C)- and R848-stimulated PBMCs. The non-parametric correlation between age and cytokine expression and differences in developmental trajectories between healthy and atopic subjects were estimated. Patterns of cytokine development were identified with principal component analysis. RESULTS: Normal innate immune maturation entails significant and progressive age-related changes in the production of IL-1ß, TNF-α, MIP-1ß, MCP-3, IP-10, IL-10, IL-12p70, and IFN-γ upon TLR3 and/or TLR7/8 stimulation. Individual cytokines made small contributions to the observed variability; chemokines MCP-3 and IP-10 were key contributors. The development of these pathways deviated in atopic subjects with significant differences observed in the trajectories of IL-1ß, MIP-1ß, and IL-10 syntheses. CONCLUSION: TLR3 and TLR7/8 pathways mature during childhood, while atopy is associated with an abnormal maturation pattern. Suboptimal responses in Th1, inflammatory cytokine, and chemokine production may be implicated in poor antiviral immunity in atopics. Moreover, the deficient maturation of IL-10 synthesis may be implicated in the breaking of tolerance, characterizing the onset of atopic disease.

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis. Methods: One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations. Results: PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, p < 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission (p < 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%). Conclusion: A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor.