Activity

To evaluate the agreement in brain injury findings between early and late magnetic resonance imaging (MRI) in newborn infants with hypoxic-ischemic encephalopathy treated with therapeutic hypothermia and to compare the ability of early vs late MRI to predict early neurodevelopmental outcomes.

This was a prospective longitudinal study of 49 patients with hypoxic-ischemic encephalopathy who underwent therapeutic hypothermia and had MRI performed at both <7 and≥7days of age. MRIs were reviewed by an experienced neuroradiologist and assigned brain injury severity scores according to established systems. Scores for early and late MRIs were assessed for agreement using the kappa statistic. The ability of early and late MRI scores to predict death or developmental delay at 15-30months of age was assessed by logistic regression analyses.

Agreement between the early and late MRI was substantial to near perfect (k>0.75, P<.001) across MRI scoring systems. In cases of discrepant scoring, early MRI was more likely to identify severe injury when compared with late MRI. Early MRI scores were more consistently predictive of adverse outcomes compared with late MRI.

The results of this study suggest that a single MRI performed in the first week after birth is adequate to assess brain injury and offer prognostic information in this high-risk population.

The results of this study suggest that a single MRI performed in the first week after birth is adequate to assess brain injury and offer prognostic information in this high-risk population.Epidermal growth factor receptor (EGFR) signaling drives the formation of many types of cancer, including colon cancer. Docosahexaenoic acid (DHA, 22∶6Δ4,7,10,13,16,19), a chemoprotective long-chain n-3 polyunsaturated fatty acid suppresses EGFR signaling. However, the mechanism underlying this phenotype remains unclear. Therefore, we used super-resolution microscopy techniques to investigate the mechanistic link between EGFR function and DHA-induced alterations to plasma membrane nanodomains. Using isogenic in vitro (YAMC and IMCE mouse colonic cell lines) and in vivo (Drosophila, wild type and Fat-1 mice) models, cellular DHA enrichment via therapeutic nanoparticle delivery, endogenous synthesis, or dietary supplementation reduced EGFR-mediated cell proliferation and downstream Ras/ERK signaling. Phospholipid incorporation of DHA reduced membrane rigidity and the size of EGFR nanoclusters. Similarly, pharmacological reduction of plasma membrane phosphatidic acid (PA), phosphatidylinositol-4,5-bisphosphate (PIP2) or cholesterol was associated with a decrease in EGFR nanocluster size. Furthermore, in DHA-treated cells only the addition of cholesterol, unlike PA or PIP2, restored EGFR nanoscale clustering. These findings reveal that DHA reduces EGFR signaling in part by reshaping EGFR proteolipid nanodomains, supporting the feasibility of using membrane therapy, i.e., dietary/drug-related strategies to target plasma membrane organization, to reduce EGFR signaling and cancer risk.Cholesteryl ester transfer protein (CETP) modulates lipoprotein metabolism by transferring cholesteryl ester (CE) and triglyceride (TG) between lipoproteins. However, differences in the way CETP functions exist across species. Unlike human CETP, hamster CETP prefers TG over CE as a substrate, raising questions regarding how substrate preference may impact lipoprotein metabolism. To understand how altering the CE versus TG substrate specificity of CETP might impact lipoprotein metabolism in humans, we modified CETP expression in fat/cholesterol-fed hamsters, which have a human-like lipoprotein profile. 3-Methyladenine Hamsters received adenoviruses expressing no CETP, hamster CETP, or human CETP. Total plasma CETP mass increased up to 70% in the hamster and human CETP groups. Hamsters expressing human CETP exhibited decreased endogenous hamster CETP, resulting in an overall CETG preference of plasma CETP that was similar to that in humans. Hamster CETP overexpression had little impact on lipoproteins, whereas human CETP expression reduced HDL by 60% without affecting LDL. HDLs were TG enriched and CE depleted and much smaller, causing the HDL3HDL2 ratio to increase threefold. HDL from hamsters expressing human CETP supported higher LCAT activity and greater cholesterol efflux. The fecal excretion of HDL-associated CE in human CETP animals was unchanged. However, much of this cholesterol accumulated in the liver and was associated with a 1.8-fold increase in hepatic cholesterol mass. Overall, these data show in a human-like lipoprotein model that modification of CETP’s lipid substrate preference selectively alters HDL concentration and function. This provides a powerful tool for modulating HDL metabolism and impacting sterol balance in vivo.Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data – including in small amounts – can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.