University of Pittsburgh

About: University of Pittsburgh is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 87042 authors who have published 201012 publications receiving 9656783 citations. The organization is also known as: Pitt & Western University of Pennsylvania.

De novo mutations revealed by whole-exome sequencing are strongly associated with autism

Abstract: Multiple studies have confirmed the contribution of rare de novo copy number variations to the risk for autism spectrum disorders. But whereas de novo single nucleotide variants have been identified in affected individuals, their contribution to risk has yet to be clarified. Specifically, the frequency and distribution of these mutations have not been well characterized in matched unaffected controls, and such data are vital to the interpretation of de novo coding mutations observed in probands. Here we show, using whole-exome sequencing of 928 individuals, including 200 phenotypically discordant sibling pairs, that highly disruptive (nonsense and splice-site) de novo mutations in brain-expressed genes are associated with autism spectrum disorders and carry large effects. On the basis of mutation rates in unaffected individuals, we demonstrate that multiple independent de novo single nucleotide variants in the same gene among unrelated probands reliably identifies risk alleles, providing a clear path forward for gene discovery. Among a total of 279 identified de novo coding mutations, there is a single instance in probands, and none in siblings, in which two independent nonsense variants disrupt the same gene, SCN2A (sodium channel, voltage-gated, type II, α subunit), a result that is highly unlikely by chance.

5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression

Abstract: Carriers of the short allele of a functional 5¢ promoter polymorphism of the serotonin transporter gene have increased anxietyrelated temperamental traits, increased amygdala reactivity and elevated risk of depression. Here, we used multimodal neuroimaging in a large sample of healthy human subjects to elucidate neural mechanisms underlying this complex genetic association. Morphometrical analyses showed reduced gray matter volume in short-allele carriers in limbic regions critical for processing of negative emotion, particularly perigenual cingulate and amygdala. Functional analysis of those regions during perceptual processing of fearful stimuli demonstrated tight coupling as a feedback circuit implicated in the extinction of negative affect. Short-allele carriers showed relative uncoupling of this circuit. Furthermore, the magnitude of coupling inversely predicted almost 30% of variation in temperamental anxiety. These genotype-related alterations in anatomy and function of an amygdalacingulate feedback circuit critical for emotion regulation implicate a developmental, systems-level mechanism underlying normal emotional reactivity and genetic susceptibility for depression. Depression is among the four leading causes of disability and disease burden throughout the world and is associated with serious medical conditions and mortality across the lifespan 1,2 . The importance of serotonergic neurotransmission for the pathogenesis of depression is suggested clinically by the efficacy of serotonin re-uptake inhibitors (SSRIs), the first-line treatment of depression and most related anxiety disorders 1 and by induction of depression by tryptophan depletion in susceptible individuals 2 . Post-mortem and in vivo studies of the serotonin transporter (5-HTT) and receptors support a role for this neurotransmitter system in depression and anxiety disorders 1 . Further

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

Abstract: Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" nodule�3 mm," " nodule<3 mm," and "non- nodule�3 mm "). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked " nodul�3 mm " by at least one radiologist, of which 928 (34.7) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice. © 2011 U.S. Government.