Baltimore, Maryland, United States

Departments View all

Laboratory of Clinical Investigation (LCI)
605
Total Impact Points
14
Members
Laboratory of Neurosciences (LNS)
3,936
Total Impact Points
13
Members
Laboratory of Molecular Gerontology (LMG)
1,801
Total Impact Points
13
Members

Publication History View all

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    ABSTRACT: During aging, progressive deleterious changes increase the risk of disease and death. Prominent molecular hallmarks of aging are genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, cellular senescence, stem cell exhaustion, and altered intercellular communication. Long noncoding RNAs (lncRNAs) play important roles in a wide range of biological processes, including age-related diseases like cancer, cardiovascular pathologies, and neurodegerative disorders. Evidence is emerging that lncRNAs influence the molecular processes that underlie age-associated phenotypes. Here, we review our current understanding of lncRNAss that control the development of aging traits.
    Aging 12/2014; 6(12).
  • Aging 06/2014; 6(6).
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    ABSTRACT: Basic research on neurocognitive aging has traditionally adopted a reductionist approach in the search for the basis of cognitive preservation versus decline. However, increasing evidence suggests that a network level understanding of the brain can provide additional novel insight into the structural and functional organization from which complex behavior and dysfunction emerge. Using graph theory as a mathematical framework to characterize neural networks, recent data suggest that alterations in structural and functional networks may contribute to individual differences in cognitive phenotypes in advanced aging. This paper reviews literature that defines network changes in healthy and pathological aging phenotypes, while highlighting the substantial overlap in key features and patterns observed across aging phenotypes. Consistent with current efforts in this area, here we outline one analytic strategy that attempts to quantify graph theory metrics more precisely, with the goal of improving diagnostic sensitivity and predictive accuracy for differential trajectories in neurocognitive aging. Ultimately, such an approach may yield useful measures for gauging the efficacy of potential preventative interventions and disease modifying treatments early in the course of aging.
    Ageing research reviews 05/2014; DOI:10.1016/j.arr.2014.02.001

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    Baltimore, Maryland, United States
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Top publications last week by downloads

 
Obesity 08/2008; 16(10):2323-30. DOI:10.1038/oby.2008.351
48 Downloads
 
CNS & neurological disorders drug targets 04/2012; 11(4):395-409. DOI:10.2174/187152712800792785
14 Downloads

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