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  1. Gibson BA, Ghosh D, Morano JP, Altice FL
    Health Place, 2014 Jul;28:153-66.
    PMID: 24853039 DOI: 10.1016/j.healthplace.2014.04.008
    We mapped mobile medical clinic (MMC) clients for spatial distribution of their self-reported locations and travel behaviors to better understand health-seeking and utilization patterns of medically vulnerable populations in Connecticut. Contrary to distance decay literature, we found that a small but significant proportion of clients was traveling substantial distances to receive repeat care at the MMC. Of 8404 total clients, 90.2% lived within 5 miles of a MMC site, yet mean utilization was highest (5.3 visits per client) among those living 11-20 miles of MMCs, primarily for those with substance use disorders. Of clients making >20 visits, 15.0% traveled >10 miles, suggesting that a significant minority of clients traveled to MMC sites because of their need-specific healthcare services, which are not only free but available at an acceptable and accommodating environment. The findings of this study contribute to the important research on healthcare utilization among vulnerable population by focusing on broader dimensions of accessibility in a setting where both mobile and fixed healthcare services coexist.
  2. Ghosh D, Krishnan A, Gibson B, Brown SE, Latkin CA, Altice FL
    AIDS Behav, 2017 Apr;21(4):1183-1207.
    PMID: 27125244 DOI: 10.1007/s10461-016-1413-y
    Social network analysis (SNA) and social network-based interventions (SNI) are important analytical tools harnessing peer and family influences critical for HIV prevention and treatment among substance users. While SNA is an effective way to measure social network influences, SNI directly or indirectly involves network members in interventions. Even though these methods have been applied in heterogeneous ways, leading to extensive evidence-based practices, systematic reviews are however, lacking. We searched five bibliographic databases and identified 58 studies involving HIV in substance users that had utilized SNA or SNI as part of their methodology. SNA was used to measure network variables as inputs in statistical/mathematical models in 64 % of studies and only 22 % of studies used SNI. Most studies focused on HIV prevention and few addressed diagnosis (k = 4), care linkage and retention (k = 5), ART adherence (k = 2), and viral suppression (k = 1). This systematic review highlights both the advantages and disadvantages of social network approaches for HIV prevention and treatment and gaps in its use for HIV care continuum.
  3. Yelamanchi SD, Tyagi A, Mohanty V, Dutta P, Korbonits M, Chavan S, et al.
    OMICS, 2018 12;22(12):759-769.
    PMID: 30571610 DOI: 10.1089/omi.2018.0160
    The pituitary function is regulated by a complex system involving the hypothalamus and biological networks within the pituitary. Although the hormones secreted from the pituitary have been well studied, comprehensive analyses of the pituitary proteome are limited. Pituitary proteomics is a field of postgenomic research that is crucial to understand human health and pituitary diseases. In this context, we report here a systematic proteomic profiling of human anterior pituitary gland (adenohypophysis) using high-resolution Fourier transform mass spectrometry. A total of 2164 proteins were identified in this study, of which 105 proteins were identified for the first time compared with high-throughput proteomic-based studies from human pituitary glands. In addition, we identified 480 proteins with secretory potential and 187 N-terminally acetylated proteins. These are the first region-specific data that could serve as a vital resource for further investigations on the physiological role of the human anterior pituitary glands and the proteins secreted by them. We anticipate that the identification of previously unknown proteins in the present study will accelerate biomedical research to decipher their role in functioning of the human anterior pituitary gland and associated human diseases.
  4. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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