19 research outputs found

    The relationship between adverse neighborhood socioeconomic context and HIV continuum of care outcomes in a diverse HIV clinic cohort in the Southern United States

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    Retention in care and viral suppression are critical to delaying HIV progression and reducing transmission. Neighborhood socioeconomic context (NSEC) may affect HIV care receipt. We therefore assessed NSEC's impact on retention and viral suppression in a diverse HIV clinical cohort. HIV-positive adults with ≥1 visit at the Vanderbilt Comprehensive Care Clinic and 5-digit ZIP code tabulation area (ZCTA) information between 2008 and 2012 contributed. NSEC z-score indices used neighborhood-level socioeconomic indicators for poverty, education, labor-force participation, proportion of males, median age, and proportion of residents of black race by ZCTA. Retention was defined as ≥2 HIV care visits per calendar year, >90 days apart. Viral suppression was defined as an HIV-1 RNA <200 copies/mL at last measurement per calendar year. Modified Poisson regression was used to estimate risk ratios (RR) and 95% confidence intervals (CI). Among 2272 and 2541 adults included for retention and viral suppression analyses, respectively, median age and CD4 count at enrollment were approximately 38 (1st and 3rd quartile: 30, 44) years and 351 (176, 540) cells/μL, respectively, while 24% were female, and 39% were black. Across 243 ZCTAs, median NSEC z-score was 0.09 (-0.66, 0.48). Overall, 79% of person-time contributed was retained and 74% was virally suppressed. In adjusted models, NSEC was not associated with retention, though being in the 4th vs. 1st NSEC quartile was associated with lack of viral suppression (RR = 0.88; 95% CI: 0.80-0.97). Residing in the most adverse NSEC was associated with lack of viral suppression. Future studies are needed to confirm this finding

    On the Threshold of the Political: The Sonic Performativity of Rooftop Chanting in Iran

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    Protests erupted throughout Iran in 2009 after incumbent Mahmood Ahmadinejad was declared winner of a highly disputed presidential race. The so-called “Green Wave” of protest included violent clashes with the Islamic Revolutionary Guard, the jailing of protesters and journalists, as well as injuries and casualties. Few foreign journalists were granted visas to enter the country, resulting in an information vacuum filled by so-called “citizen journalists” who uploaded cell-phone videos of and tweeted about the violent clashes. Alongside this emerged video chronicles of nightly chants by residents of Iran's densely populated cities shouting “Allah-O-Akbar” from their rooftops. By tracing the roots of this protest tradition, not only in the Iranian revolution of 1979 but also in Shi'a rowzeh khani performance, this essay examines rooftop chanting as an enactment of a counter-politics through sonic performativity. The threshold space of the rooftop figures here as a space of political improvisation

    Genetic Algorithm-Based Sliding Mode Control of a Human Arm Model

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    Spinal cord injured patients cannot move their segments by their intact muscles. A suitable controller can be used to help them move their arm. In this study, the kinematics and dynamics of right-hand movement are modeled considering planar three links. A genetic algorithm-based sliding mode (GASM) controller is designed to move the human arm model for tracking a desired trajectory in the sagittal plane. The GA is used to tune the convergence rate of the sliding mode controller for having an appropriate tracking performance. The summation of errors is considered as a cost function and GA is proposed to find the controller gains to minimize the difference between the outputs of the model and nominal trajectories. To the best of the author\u27s knowledge, it is for the first time that the GA-sliding mode controller has been used for controlling the human hand so as to have a particular movement. Simulation results are evaluated in upward and downward movements of the human arm to affirm the effectiveness of the proposed controller

    The role of selenium in the secretion of very-low-density lipoprotein in the isolated perfused rat liver.

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    A recirculating liver perfusion system was used to study the effects of dietary selenium (Se) on the hepatic secretion of very-low-density lipoprotein (VLDL). The perfusate from livers of rats fed on a Se-deficient diet incorporated about 50% more [1-14C]oleic acid into triacylglycerol (TG) and cholesteryl esters (ChoEs) than did the perfusate from livers of rats fed on a Se-supplemented diet. Similarly, livers from rats fed the Se-deficient diet secreted more VLDL and incorporated about 60% more [1-14C]oleic acid into VLDL TG and ChoEs than did livers from rats fed the Se-supplemented diet. The liver perfusate from rats in the Se-deficient group also showed significantly decreased fatty acid oxidation. We conclude that Se is a potent modulator of lipoprotein metabolism. A primary action of Se deficiency appears to be a decrease in fatty acid oxidation and a stimulation of fatty acid esterification, leading to increased VLDL TG and ChoEs formation and secretion

    Personalised drug prescription for dental clinics using word embedding

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    The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drug-drug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new medication prescribed must be checked against the patient’s medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patient’s medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication against the patient’s medical profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a three-tier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http://r.glory.sg/main.php). By engaging with the interactive demonstration, participants will gain first-hand experience in the process from research idea to implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students
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