Institution
Drexel University
Education•Philadelphia, Pennsylvania, United States•
About: Drexel University is a education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 26770 authors who have published 51438 publications receiving 1949443 citations. The organization is also known as: Drexel & Drexel Institute.
Papers published on a yearly basis
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University of Texas Health Science Center at Houston1, University of Toronto2, Stony Brook University3, University of New Mexico4, University of Maryland, Baltimore5, Icahn School of Medicine at Mount Sinai6, Maimonides Medical Center7, University of British Columbia8, Université de Montréal9, University of Ottawa10, University of Paris11, Queen's University12, Allegheny General Hospital13, Henry Ford Health System14, University of Pennsylvania15, Indiana University – Purdue University Indianapolis16, Mayo Clinic17, Drexel University18, Shepherd Center19, Ohio State University20, Oregon Health & Science University21, State University of New York Upstate Medical University22, Roswell Park Cancer Institute23, University of California, Davis24, University of California, Irvine25, University of California, Los Angeles26, University of California, San Diego27, University of California, San Francisco28, University of Chicago29, University of Alabama at Birmingham30, University of Kansas31, Rutgers University32, University of Miami33, University of Pittsburgh34, University of Rochester35, University of Southern California36, University of South Florida37, University of Texas Southwestern Medical Center38, University of Washington39, University of Wisconsin-Madison40, University of Utah41, Vanderbilt University42, Wake Forest University43, Washington University in St. Louis44, Wayne State University45, Yale University46, McGill University47, Foothills Medical Centre48, National Institutes of Health49
TL;DR: To determine whether glatiramer acetate slows accumulation of disability in primary progressive multiple sclerosis, a new drug is developed that acts as a ‘spatially aggregating agent’ to reduce the risk of disease progression.
Abstract: Objective
To determine whether glatiramer acetate (GA) slows accumulation of disability in primary progressive multiple sclerosis.
Methods
A total of 943 patients with primary progressive multiple sclerosis were randomized to GA or placebo (PBO) in this 3-year, double-blind trial. The primary end point was an intention-to-treat analysis of time to 1- (entry expanded disability status scale, 3.0–5.0) or 0.5-point expanded disability status scale change (entry expanded disability status scale, 5.5–6.5) sustained for 3 months. The trial was stopped after an interim analysis by an independent data safety monitoring board indicated no discernible treatment effect on the primary outcome. Intention-to-treat analyses of disability and magnetic resonance imaging end points were performed.
Results
There was a nonsignificant delay in time to sustained accumulated disability in GA- versus PBO-treated patients (hazard ratio, 0.87 [95% confidence interval, 0.71–1.07]; p = 0.1753), with significant decreases in enhancing lesions in year 1 and smaller increases in T2 lesion volumes in years 2 and 3 versus PBO. Post hoc analysis showed that survival curves for GA-treated male patients diverged early from PBO-treated male subjects (hazard ratio, 0.71 [95% confidence interval, 0.53–0.95]; p = 0.0193).
Interpretation
The trial failed to demonstrate a treatment effect of GA on primary progressive multiple sclerosis. Both the unanticipated low event rate and premature discontinuation of study medication decreased the power to detect a treatment effect. Post hoc analysis suggests GA may have slowed clinical progression in male patients who showed more rapid progression when untreated. Ann Neurol 2007;61:14–24
406 citations
TL;DR: It is proved that for controllable quantum systems with no constraints placed on the controls, the only allowed extrema of the transition probability landscape correspond to perfect control or no control.
Abstract: A large number of experimental studies and simulations show that it is surprisingly easy to find excellent quality control over broad classes of quantum systems. We now prove that for controllable quantum systems with no constraints placed on the controls, the only allowed extrema of the transition probability landscape correspond to perfect control or no control. Under these conditions, no suboptimal local extrema exist as traps that would impede the search for an optimal control. The identified landscape structure is universal for all controllable quantum systems of the same dimension when seeking to maximize the same transition probability, regardless of the detailed nature of the system Hamiltonian. The presence of weak control field noise or environmental decoherence is shown to preserve the general structure of the control landscape, but at lower resolution.
405 citations
405 citations
TL;DR: The study shows that dispensing pressure has a more significant effect on cell viability than the nozzle diameter, and incorporates an analytical formulation to predict the cell viability through the system as a function of the maximum shear stress in the system.
Abstract: Bioprinting is an emerging technology in the field of tissue engineering and regenerative medicine. The process consists of simultaneous deposition of cells, biomaterial and/or growth factors under pressure through a micro-scale nozzle. Cell viability can be controlled by varying the parameters like pressure and nozzle diameter. The process itself can be a very useful tool for evaluating an in vitro cell injury model. It is essential to understand the cell responses to process-induced mechanical disturbances because they alter cell morphology and function. We carried out analysis and quantification of the degree of cell injury induced by bioprinting process. A parametric study with different process parameters was conducted to analyze and quantify cell injury as well as to optimize the parameters for printing viable cells. A phenomenological model was developed correlating the percentage of live, apoptotic and necrotic cells to the process parameters. This study incorporates an analytical formulation to predict the cell viability through the system as a function of the maximum shear stress in the system. The study shows that dispensing pressure has a more significant effect on cell viability than the nozzle diameter. The percentage of live cells is reduced significantly (by 38.75%) when constructs are printed at 40 psi compared to those printed at 5 psi.
405 citations
King's College London1, University of Texas MD Anderson Cancer Center2, Pasteur Institute3, Monell Chemical Senses Center4, Oregon Health & Science University5, University of Colorado Boulder6, Drexel University7, Pennsylvania State University8, Wadsworth Center9, Leibniz Association10, Health Canada11, University of Tennessee Health Science Center12, Washington University in St. Louis13, University of Memphis14, University of Massachusetts Medical School15, Hebrew University of Jerusalem16, University of Groningen17, Roswell Park Cancer Institute18, Purdue University19, University of California, Davis20, University of Oxford21, University of Texas Southwestern Medical Center22, International Livestock Research Institute23, Max Planck Society24, University of Alabama at Birmingham25, National Institutes of Health26, Charité27, RWTH Aachen University28, University of California, Los Angeles29, McGill University30, Royal Melbourne Hospital31, Rutgers University32, Stanford University33, Columbia University34, Princeton University35, University of Nebraska–Lincoln36, Harvard University37, University of Toronto38, Vanderbilt University39, Northwestern University40, Shriners Hospitals for Children41, University of Colorado Denver42, Thomas Jefferson University43, University of Vermont44, University of North Carolina at Chapel Hill45, Southern Illinois University Carbondale46, Medical Research Council47, New York University48, University of Kentucky49
TL;DR: This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits.
Abstract: This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?
404 citations
Authors
Showing all 26976 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| John Q. Trojanowski | 226 | 1467 | 213948 |
| Peter Libby | 211 | 932 | 182724 |
| Virginia M.-Y. Lee | 194 | 993 | 148820 |
| Yury Gogotsi | 171 | 956 | 144520 |
| Dennis R. Burton | 164 | 683 | 90959 |
| M.-Marsel Mesulam | 150 | 558 | 90772 |
| Edward G. Lakatta | 146 | 858 | 88637 |
| Gordon T. Richards | 144 | 613 | 110666 |
| David Price | 138 | 1687 | 93535 |
| Joseph Sodroski | 138 | 542 | 77070 |
| Hannu Kurki-Suonio | 138 | 433 | 99607 |
| Jun Lu | 135 | 1526 | 99767 |
| Stephen F. Badylak | 133 | 530 | 57083 |
| Michael E. Thase | 131 | 923 | 75995 |
| Edna B. Foa | 129 | 588 | 73034 |