Georgetown University

About: Georgetown University is a education organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 23377 authors who have published 43718 publications receiving 1748598 citations. The organization is also known as: GU & Georgetown.

A Theoretical Framework for Understanding Help‐Seeking Processes Among Survivors of Intimate Partner Violence

Abstract: This paper suggests a conceptual framework for understanding the processes of help-seeking among survivors of intimate partner violence (IPV). A cognitive theory from general literature on help-seeking in "stigmatizing" situations suggests three relevant processes or stages of seeking help in the IPV context: defining the problem, deciding to seek help, and selecting a source of support. Individual, interpersonal, and sociocultural factors that influence decision-making at each of these stages are discussed and illustrated with case examples.

Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Abstract: Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

APOL1 Risk Variants, Race, and Progression of Chronic Kidney Disease

Abstract: Methods In two studies, we examined the effects of variants in the gene encoding apolipoprotein L1 (APOL1) on the progression of chronic kidney disease. In the African American Study of Kidney Disease and Hypertension (AASK), we evaluated 693 black patients with chronic kidney disease attributed to hypertension. In the Chronic Renal Insufficiency Cohort (CRIC) study, we evaluated 2955 white patients and black patients with chronic kidney disease (46% of whom had diabetes) according to whether they had 2 copies of high-risk APOL1 variants (APOL1 high-risk group) or 0 or 1 copy (APOL1 low-risk group). In the AASK study, the primary outcome was a composite of end-stage renal disease or a doubling of the serum creatinine level. In the CRIC study, the primary outcomes were the slope in the estimated glomerular filtration rate (eGFR) and the composite of end-stage renal disease or a reduction of 50% in the eGFR from baseline. Results In the AASK study, the primary outcome occurred in 58.1% of the patients in the APOL1 high-risk group and in 36.6% of those in the APOL1 low-risk group (hazard ratio in the high-risk group, 1.88; P<0.001). There was no interaction between APOL1 status and trial interventions or the presence of baseline proteinuria. In the CRIC study, black patients in the APOL1 high-risk group had a more rapid decline in the eGFR and a higher risk of the composite renal outcome than did white patients, among those with diabetes and those without diabetes (P<0.001 for all comparisons). Conclusions Renal risk variants in APOL1 were associated with the higher rates of end-stage renal disease and progression of chronic kidney disease that were observed in black patients as compared with white patients, regardless of diabetes status. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others.)

Autoregressive conditional skewness

Abstract: We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.