Spirometric phenotypes from early childhood to young adulthood: a Chronic Airway Disease Early Stratification study.

Background: The prevalences of obstructive and restrictive spirometric phenotypes, and their relation to early-life risk factors from childhood to young adulthood remain poorly understood. The aim was to explore these phenotypes and associations with well-known respiratory risk factors across ages and populations in European cohorts.

Methods: We studied 49 334 participants from 14 population-based cohorts in different age groups (≤10, >10-15, >15-20, >20-25 years, and overall, 5-25 years).

Medicinal plants and natural products as neuroprotective agents in age-related macular degeneration.

The retina may suffer neurodegenerative damages, as other tissues of the central nervous system do, and serious eye diseases may develop. One of them is age-related macular degeneration, which causes progressive loss of vision due to retina degeneration. Treatment of age-related macular degeneration focuses on antioxidant agents and anti-vascular endothelial growth factor compounds, among others, that prevent/diminish oxidative stress and reduce neovascularisation respectively.

Neuroprotective Potential of Ginkgo biloba in Retinal Diseases.

Like other tissues of the central nervous system, the retina is susceptible to damage by oxidative processes that result in several neurodegenerative disease such as age-related macular degeneration, diabetic retinopathy, glaucoma, ischaemic retinal disease, retinal disease produced by light oxidation, and detached retina, among other diseases. The use of antioxidant substances is a solution to some health problems caused by oxidative stress, because they regulate redox homeostasis and reduce oxidative stress. This is important for neurodegeneration linked to oxidation processes.

Bayesian joint modeling for assessing the progression of chronic kidney disease in children.

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation.