There is a close relationship between sleep and depression, and certain maladaptive outcomes of sleep problems may only be apparent in individuals with heightened levels of depression. In a sample enriched for preschool depression, we examined how sleep and depression in early childhood interact to predict later trajectories of gray matter volume. Participants (N = 161) were recruited and assessed during preschool (ages 3–6 years) and were later assessed with five waves of structural brain imaging, spanning from late childhood to adolescence. Sleep and depression were assessed using a semi-structured parent interview when the children were preschool-aged, and total gray matter volume was calculated at each scan wave. Although sleep disturbances alone did not predict gray matter volume/trajectories, preschool sleep and depression symptoms interacted to predict later total gray matter volume and the trajectory of decline in total gray matter volume. Sleep disturbances in the form of longer sleep onset latencies, increased irregularity in the child’s sleep schedule, and higher levels of daytime sleepiness in early childhood were all found to interact with early childhood depression severity to predict later trajectories of cortical gray matter volume. Findings provide evidence of the interactive effects of preschool sleep and depression symptoms on later neurodevelopment. 相似文献
In this study, total body clearance (CLt), volume of distribution at steady state (Vss) and plasma concentration–time profiles in humans of model compounds were predicted using chimeric mice with humanized livers.
On the basis of assumption that unbound intrinsic clearance (CLUint) per liver weight in chimeric mice was equal to those in humans, CLt were predicted by substituting human liver blood flow and liver weights in well-stirred model. Vss were predicted by Rodgers equation using scaling factors of tissue-plasma concentration ratios (SFKp) in chimeric mice estimated from a difference between the observed and predicted Vss. These physiological approaches showed high prediction accuracy for CLt and Vss values in humans.
We compared the predictability of CLt and Vss determined by the physiologically based predictive approach using chimeric mice with those from predictive methods reported by Pharmaceutical Research Manufacturers of America. The physiological approach using chimeric mice indicated the best prediction accuracy in each predictive method.
Simulation of human plasma concentration–time profiles were generally successful with physiologically based pharmacokinetic (PBPK) model incorporating CLUint and SFKp obtained from chimeric mice.
Combined application of chimeric mice and PBPK modeling is effective for prediction of human PK in various compounds.