\u3ci\u3eIn Vitro\u3c/i\u3e Validation of Patient-Specific Hemodynamic Simulations in Coronary Aneurysms Caused by Kawasaki Disease

To perform experimental validation of computational fluid dynamics (CFD) applied to patient specific coronary aneurysm anatomy of Kawasaki disease. We quantified hemodynamics in a patient-specific coronary artery aneurysm physical phantom under physiologic rest and exercise flow conditions. Using phase contrast MRI (PCMRI), we acquired 3-component flow velocity at two slice locations in the aneurysms. We then performed numerical simulations with the same geometry and inflow conditions, and performed qualitative and quantitative comparisons of velocities between experimental measurements and simulation results. We observed excellent qualitative agreement in flow pattern features. The quantitative spatially and temporally varying differences in velocity between PCMRI and CFD were proportional to the flow velocity. As a result, the percent discrepancy between simulation and experiment was relatively constant regardless of flow velocity variations. Through 1D and 2D quantitative comparisons, we found a 5–17% difference between measured and simulated velocities. Additional analysis assessed wall shear stress differences between deformable and rigid wall simulations. This study demonstrated that CFD produced good qualitative and quantitative predictions of velocities in a realistic coronary aneurysm anatomy under physiological flow conditions. The results provide insights on factors that may influence the level of agreement, and a set of in vitro experimental data that can be used by others to compare against CFD simulation results. The findings of this study increase confidence in the use of CFD for investigating hemodynamics in the specialized anatomy of coronary aneurysms. This provides a basis for future hemodynamics studies in patient-specific models of Kawasaki disease

Characterization of a 3D matrix bioreactor for scaled production of human mesenchymal stem cells

Human Mesenchymal Stem Cells (hMSCs) are multipotent, immune-privileged, and possess the capacity to proliferate ex-vivo, making them a good candidate for stem cell therapy. However, a reliable scalable production system for hMSCs is needed to fuel the growing field of regenerative medicine. Current growth of hMSCs is achieved through adherent 2D methods using tissue culture flasks or cell factory systems. These processes are labor intensive and can lead to low purity and poor yield of hMSCs due to the limited control of culture conditions inherent in these systems. In this work, we are investigating a novel 3D honeycomb matrix culture system for controlled high density hMSC production. We have assessed compatibility of the hMSCs on the honeycomb matrix and developed a scale down model bioreactor for development and characterization. Computational Fluid Dynamic (CFD) modeling is used in parallel with the described in-vitro experimentation to characterize shear profiles and oxygen transport for optimization of the conditions to support high cell density hMSC cultures. These techniques will potentially allow for higher yield and purity of hMSCs to meet the large quantities of cells needed for emerging whole cell therapies

Genome-wide gene by environment study of time spent in daylight and chronotype identifies emerging genetic architecture underlying light sensitivity

Study Objectives: Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interindividual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interindividual differences.Methods: We performed a genome-wide gene-by-environment interaction study (GWIS) in 280 897 individuals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (individual time of day preference), acting as “light sensitivity” variants for the impact of daylight on the circadian system.Results: We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634; p < 5 × 10−8), where additional minor alleles were found to enhance the morningness effect of daytime light exposure (βGxE = −.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and genes expressed in Per2+ hypothalamic neurons. Linkage disequilibrium score regression identified Bonferroni significant genetic correlations of greater light sensitivity GWIS with later chronotype and shorter sleep duration. Greater light sensitivity was nominally genetically correlated with insomnia symptoms and risk for post-traumatic stress disorder (PTSD).Conclusions: This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental healt

Characterizing the role of brain derived neurotrophic factor genetic variation in Alzheimer’s Disease neurodegeneration

There is accumulating evidence that neurotrophins, like brain-derived neurotrophic factor (BDNF), may impact aging and Alzheimer's Disease. However, traditional genetic association studies have not found a clear relationship between BDNF and AD. Our goal was to test whether BDNF single nucleotide polymorphisms (SNPs) impact Alzheimer's Disease-related brain imaging and cognitive markers of disease. We completed an imaging genetics study on 645 Alzheimer's Disease Neuroimaging Initiative participants (ND=175, MCI=316, AD=154) who had cognitive, brain imaging, and genetics data at baseline and a subset of those with brain imaging data at two years. Samples were genotyped using the Illumina Human610-Quad BeadChip. 13 SNPs in BDNF were identified in the dataset following quality control measures (rs6265(Val66Met), rs12273363, rs11030094, rs925946, rs1050187, rs2203877, rs11030104, rs11030108, rs10835211, rs7934165, rs908867, rs1491850, rs1157459). We analyzed a subgroup of 8 SNPs that were in low linkage disequilibrium with each other. Automated brain morphometric measures were available through ADNI investigators, and we analyzed baseline cognitive scores, hippocampal and whole brain volumes, and rates of hippocampal and whole brain atrophy and rates of change in the ADAS-Cog over one and two years. Three out of eight BDNF SNPs analyzed were significantly associated with measures of cognitive decline (rs1157659, rs11030094, rs11030108). No SNPs were significantly associated with baseline brain volume measures, however six SNPs were significantly associated with hippocampal and/or whole brain atrophy over two years (rs908867, rs11030094, rs6265, rs10501087, rs1157659, rs1491850). We also found an interaction between the BDNF Val66Met SNP and age with whole brain volume. Our imaging-genetics analysis in a large dataset suggests that while BDNF genetic variation is not specifically associated with a diagnosis of AD, it appears to play a role in AD-related brain neurodegeneration

Future Directions in the Developmental Science of Addictions

This essay addresses important future directions for the study of addictions, emphasizing the incorporation of developmental perspectives into how we think about substance use and disorder as unfolding processes over time and context for a heterogeneous group of individuals. These perspectives articulate complexities in the developmental processes that underlie change and continuity in human behavior over time. We consider two key developmental concepts, namely ‘time’ and ‘heterogeneity’. We argue that a lack of attention to time sampling creates ambiguity in the meaning of time-linked assessments, challenges in discerning which of multiple clocks may govern behavior, and the inability in some instances to distinguish which of multiple etiological processes may be driving behavior within our samples. Moreover, artificial divisions among disorders that commonly co-occur with substance use are a barrier to the further integration of the study and treatment of addictions with that of psychopathology. Similar to recent changes in the study of psychiatric disorders more broadly, we argue that identifying common deficits among commonly comorbid disorders, rather than patterns of comorbidity per se, is key to identifying early emerging risk factors for substance use and disorder, with important implications for identifying risk populations and developmental periods as well as potentially malleable intervention targets. Attention to time sampling in theory-driven research designs and attempts to identify more homogenous groups of individuals who use and eventually abuse substances over time are two examples of ways to better understand some of the complexity underlying the development of addictions