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Institution

Wayne State University

EducationDetroit, Michigan, United States
About: Wayne State University is a(n) education organization based out in Detroit, Michigan, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 42801 authors who have published 82738 publication(s) receiving 3083713 citation(s). The organization is also known as: WSU & Wayne University.
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Journal ArticleDOI
Carly Boye1, Kyle Christensen, Kamal Asadipour2, Scott DeClemente2  +2 moreInstitutions (2)
Abstract: Tendon injuries are a common athletic injury that have been increasing in prevalence. While there are current clinical treatments for tendon injuries, they have relatively long recovery times and often do not restore native function of the tendon. In the current study, gene electrotransfer (GET) parameters for delivery to the skin were optimized with monophasic and biphasic pulses with reporter and effector genes towards optimizing underlying tendon healing. Tissue twitching and damage, as well as gene expression and distribution were evaluated. Bioprinted collagen scaffolds, mimicking healthy tendon structure were then implanted subcutaneously for biocompatibility and angiogenesis analyses when combined with GET to accelerate healing. GET of human fibroblast FGF2 significantly increased angiogenesis and biocompatibility of the bioprinted implants when compared to implant only sites. The combination of bioprinted collagen fibers and angiogenic GET therapy may lead to better graft biocompatibility in tendon repair.

Journal ArticleDOI
Carly Boye1, Sezgi Arpag, Michael Francis, Scott DeClemente2  +3 moreInstitutions (3)
Abstract: Gene therapy has a wide range of applications for various types of pathologies. Viral methods of gene delivery provide high levels of gene expression but have various safety concerns. Non-viral methods are largely known to provide lower levels of expression. We aim to address this issue by using plasmid DNA with smaller viral backbones to increase gene expression levels when delivered using non-viral methods. In this study we compare gene expression levels between two vectors with firefly luciferase encoding gene insert using liposome complexes and gene electrotransfer as delivery methods. A 2-fold reduction in plasmid vector backbone size, disproportionately enhanced gene expression levels more than 10-fold in rat tenocytes in vitro, and rat myocardium in vivo, while improvements in delivery to the skin were more moderate.

Journal ArticleDOI
Riley Mulhern1, Javad Roostaei1, Sara Schwetschenau2, Tejas Pruthi1  +2 moreInstitutions (3)
Abstract: Lead in drinking water continues to put children at risk of irreversible neurological impairment. Understanding drinking water system characteristics that influence blood lead levels is needed to prevent ongoing exposures. This study sought to assess the relationship between children's blood lead levels and drinking water system characteristics using machine-learned Bayesian networks. Blood lead records from 2003 to 2017 for 40,742 children in Wake County, North Carolina were matched with the characteristics of 178 community water systems and sociodemographic characteristics of each child's neighborhood. Bayesian networks were machine-learned to evaluate the drinking water variables associated with blood lead levels ≥2 μg/dL and ≥5 μg/dL. The model was used to predict geographic areas and water utilities with increased lead exposure risk. Drinking water characteristics were not significantly associated with children's blood lead levels ≥5 μg/dL but were important predictors of blood lead levels ≥2 μg/dL. Whether 10% of water samples exceeded 2 ppb of lead in the most recent year prior to the blood test was the most important water system predictor and increased the risk of blood lead levels ≥2 μg/dL by 42%. The model achieved an area under the receiver operating characteristic curve of 0.792 (±0.8%) during ten-fold cross validation, indicating good predictive performance. Water system characteristics may thus be used to predict areas that are at risk of higher blood lead levels. Current drinking water regulatory thresholds for lead may be insufficient to detect the levels in drinking water associated with children's blood lead levels.

Journal ArticleDOI
Abstract: In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem ( ${{\sf ERAP}}$ ERAP ) as a Mixed-Integer Linear Program ( ${{\sf MILP}}$ MILP ) and prove that ${{\sf ERAP}}$ ERAP is ${{\sf NP}}$ NP -hard. To solve the problem efficiently, we propose two resource allocation mechanisms. First, we develop an auction-based mechanism and prove that the proposed mechanism is individually-rational and produces envy-free allocations . We also propose an ${{\sf LP}}$ LP -based approximation mechanism that does not guarantee envy-freeness, but it provides solutions that are guaranteed to be within a given distance from the optimal solution. We evaluate the performance of the proposed mechanisms by conducting an extensive experimental analysis on ${{\sf ERAP}}$ ERAP instances of various sizes. We use the optimal solutions obtained by solving the ${{\sf MILP}}$ MILP model using a commercial solver as benchmarks to evaluate the quality of solutions. Our analysis shows that the proposed mechanisms obtain near optimal solutions for fairly large size instances of the problem in a reasonable amount of time.

1 citations


Authors

Showing all 42801 results

NameH-indexPapersCitations
Robert Langer2812324326306
Eugene Braunwald2301711264576
Rakesh K. Jain2001467177727
Anil K. Jain1831016192151
Richard A. Gibbs172889249708
Bradley Cox1692150156200
Jun Wang1661093141621
David Altshuler162345201782
Elliott M. Antman161716179462
Jovan Milosevic1521433106802
Roberto Romero1511516108321
Kypros H. Nicolaides147130287091
John F. Hartwig14571466472
Charles Maguire142119795026
Mingshui Chen1411543125369
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202241
20213,533
20203,508
20193,011
20182,963
20173,126