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Institution

Medical University of South Carolina

EducationCharleston, South Carolina, United States
About: Medical University of South Carolina is a education organization based out in Charleston, South Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23436 authors who have published 45440 publications receiving 1769397 citations. The organization is also known as: MUSC & Medical College of the State of South Carolina.
Topics: Population, Poison control, Medicine, Cancer, Stroke


Papers
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Book ChapterDOI
TL;DR: This chapter will pay tribute to the complex regulation of simple sphingolipids, a class of lipids defined by their eighteen carbon amino-alcohol backbones that play significant roles in membrane biology and provide many bioactive metabolites that regulate cell function.
Abstract: Sphingolipids constitute a class of lipids defined by their eighteen carbon amino-alcohol backbones which are synthesized in the ER from nonsphingolipid precursors Modification of this basic structure is what gives rise to the vast family of sphingolipids that play significant roles in membrane biology and provide many bioactive metabolites that regulate cell function Despite the diversity of structure and function of sphingolipids, their creation and destruction are governed by common synthetic and catabolic pathways In this regard, sphingolipid metabolism can be imagined as an array of interconnected networks that diverge from a single common entry point and converge into a single common breakdown pathway In their simplest forms, sphingosine, phytosphingosine and dihydrosphingosine serve as the backbones upon which further complexity is achieved For example, phosphorylation of the C1 hydroxyl group yields the final breakdown products and/or the important signaling molecules sphingosine-1-phosphate, phytosphingosine-1-phosphate and dihydrosphingosine-1-phosphate, respectively On the other hand, acylation of sphingosine, phytosphingosine, or dihydrosphingosine with one of several possible acyl CoA molecules through the action of distinct ceramide synthases produces the molecules defined as ceramide, phytoceramide, or dihydroceramide Ceramide, due to the differing acyl CoAs that can be used to produce it, is technically a class of molecules rather than a single molecule and therefore may have different biological functions depending on the acyl chain it is composed of At the apex of complexity is the group of lipids known as glycosphingolipids (GSL) which contain dozens of different sphingolipid species differing by both the order and type of sugar residues attached to their headgroups Since these molecules are produced from ceramide precursors, they too may have differences in their acyl chain composition, revealing an additional layer of variation The glycosphingolipids are divided broadly into two categories: glucosphingolipids and galactosphingolipids The glucosphingolipids depend initially on the enzyme glucosylceramide synthase (GCS) which attaches glucose as the first residue to the C1 hydroxyl position Galactosphingolipids, on the other hand, are generated from galactosylceramide synthase (GalCerS), an evolutionarily dissimilar enzyme from GCS Glycosphingolipids are further divided based upon further modification by various glycosyltransferases which increases the potential variation in lipid species by several fold Far more abundant are the sphingomyelin species which are produced in parallel with glycosphingolipids, however they are defined by a phosphocholine headgroup rather than the addition of sugar residues Although sphingomyelin species all share a common headgroup, they too are produced from a variety of ceramide species and therefore can have differing acyl chains attached to their C-2 amino groups Whether or not the differing acyl chain lengths in SMs dictate unique functions or important biophysical distinctions has not yet been established Understanding the function of all the existing glycosphingolipids and sphingomyelin species will be a major undertaking in the future since the tools to study and measure these species are only beginning to be developed (see Fig 1 for an illustrated depiction of the various sphingolipid structures) The simple sphingolipids serve both as the precursors and the breakdown products of the more complex ones Importantly, in recent decades, these simple sphingolipids have gained attention for having significant signaling and regulatory roles within cells In addition, many tools have emerged to measure the levels of simple sphingolipids and therefore have become the focus of even more intense study in recent years With this thought in mind, this chapter will pay tribute to the complex sphingolipids, but focus on the regulation of simple sphingolipid metabolism

815 citations

Journal ArticleDOI
TL;DR: The D1-like and D2-like classes of dopamine receptors each has shared signaling properties that contribute to the definition of the receptor class, although some differences among subtypes within a class have been identified.
Abstract: The D1-like (D1, D5) and D2-like (D2, D3, D4) classes of dopamine receptors each has shared signaling properties that contribute to the definition of the receptor class, although some differences among subtypes within a class have been identified. D1-like receptor signaling is mediated chiefly by the heterotrimeric G proteins Gαs and Gαolf, which cause sequential activation of adenylate cyclase, cylic AMP-dependent protein kinase, and the protein phosphatase-1 inhibitor DARPP-32. The increased phosphorylation that results from the combined effects of activating cyclic AMP-dependent protein kinase and inhibiting protein phosphatase 1 regulates the activity of many receptors, enzymes, ion channels, and transcription factors. D1 or a novel D1-like receptor also signals via phospholipase C-dependent and cyclic AMP-independent mobilization of intracellular calcium. D2-like receptor signaling is mediated by the heterotrimeric G proteins Gαi and Gαo. These pertussis toxin-sensitive G proteins regulate some effec...

812 citations

Book
19 Oct 2007
TL;DR: The definition of ADHD in Adults was made in the UMASS and Milwaukee Studies using criteria from the DSM-5 and the issue of Age of Onset was considered.
Abstract: Introduction. History and Prevalence of ADHD in Adults. Diagnostic Criteria for ADHD in Adults. Defining ADHD in Adults: Making the Diagnosis in the UMASS and Milwaukee Studies. DSM Symptom Utility and the Issue of Age of Onset. Impairment in Major Life Activities. Identifying New Symptoms of ADHD in Adulthood. Comorbid Psychiatric Disorders and Psychological Maladjustment. Educational and Occupational Functioning. Drug Use and Antisocial Behavior. Health, Lifestyle, Money Management, and Driving. Sex, Dating and Marriage, Parenting, and Psychological Adjustment of Offspring. Neuropsychological Functioning. Summary, Conclusions, and Treatment Implications.

810 citations

Journal ArticleDOI
Lorenzo Galluzzi1, J M Bravo-San Pedro2, Ilio Vitale, Stuart A. Aaronson3, John M. Abrams4, Dieter Adam5, Emad S. Alnemri6, Lucia Altucci7, David W. Andrews8, Margherita Annicchiarico-Petruzzelli, Eric H. Baehrecke9, Nicolas G. Bazan10, Mathieu J.M. Bertrand11, Mathieu J.M. Bertrand12, Katiuscia Bianchi13, Katiuscia Bianchi14, Mikhail V. Blagosklonny15, Klas Blomgren16, Christoph Borner17, Dale E. Bredesen18, Dale E. Bredesen19, Catherine Brenner20, Catherine Brenner21, Michelangelo Campanella22, Eleonora Candi23, Francesco Cecconi23, Francis Ka-Ming Chan9, Navdeep S. Chandel24, Emily H. Cheng25, Jerry E. Chipuk3, John A. Cidlowski26, Aaron Ciechanover27, Ted M. Dawson28, Valina L. Dawson28, V De Laurenzi29, R De Maria, Klaus-Michael Debatin30, N. Di Daniele23, Vishva M. Dixit31, Brian David Dynlacht32, Wafik S. El-Deiry33, Gian Maria Fimia34, Richard A. Flavell35, Simone Fulda36, Carmen Garrido37, Marie-Lise Gougeon38, Douglas R. Green, Hinrich Gronemeyer39, György Hajnóczky6, J M Hardwick28, Michael O. Hengartner40, Hidenori Ichijo41, Bertrand Joseph16, Philipp J. Jost42, Thomas Kaufmann43, Oliver Kepp2, Daniel J. Klionsky44, Richard A. Knight22, Richard A. Knight45, Sharad Kumar46, Sharad Kumar47, John J. Lemasters48, Beth Levine49, Beth Levine50, Andreas Linkermann5, Stuart A. Lipton, Richard A. Lockshin51, Carlos López-Otín52, Enrico Lugli, Frank Madeo53, Walter Malorni54, Jean-Christophe Marine55, Seamus J. Martin56, J-C Martinou57, Jan Paul Medema58, Pascal Meier, Sonia Melino23, Noboru Mizushima41, Ute M. Moll59, Cristina Muñoz-Pinedo, Gabriel Núñez44, Andrew Oberst60, Theocharis Panaretakis16, Josef M. Penninger, Marcus E. Peter24, Mauro Piacentini23, Paolo Pinton61, Jochen H. M. Prehn62, Hamsa Puthalakath63, Gabriel A. Rabinovich64, Kodi S. Ravichandran65, Rosario Rizzuto66, Cecília M. P. Rodrigues67, David C. Rubinsztein68, Thomas Rudel69, Yufang Shi70, Hans-Uwe Simon43, Brent R. Stockwell49, Brent R. Stockwell71, Gyorgy Szabadkai66, Gyorgy Szabadkai22, Stephen W.G. Tait72, H. L. Tang28, Nektarios Tavernarakis73, Nektarios Tavernarakis74, Yoshihide Tsujimoto, T Vanden Berghe11, T Vanden Berghe12, Peter Vandenabeele12, Peter Vandenabeele11, Andreas Villunger75, Erwin F. Wagner76, Henning Walczak22, Eileen White77, W. G. Wood78, Junying Yuan79, Zahra Zakeri80, Boris Zhivotovsky81, Boris Zhivotovsky16, Gerry Melino23, Gerry Melino45, Guido Kroemer1 
Paris Descartes University1, Institut Gustave Roussy2, Mount Sinai Hospital3, University of Texas Southwestern Medical Center4, University of Kiel5, Thomas Jefferson University6, Seconda Università degli Studi di Napoli7, University of Toronto8, University of Massachusetts Medical School9, Louisiana State University10, Flanders Institute for Biotechnology11, Ghent University12, Queen Mary University of London13, Cancer Research UK14, Roswell Park Cancer Institute15, Karolinska Institutet16, University of Freiburg17, Buck Institute for Research on Aging18, University of California, San Francisco19, Université Paris-Saclay20, French Institute of Health and Medical Research21, University College London22, University of Rome Tor Vergata23, Northwestern University24, Memorial Sloan Kettering Cancer Center25, National Institutes of Health26, Technion – Israel Institute of Technology27, Johns Hopkins University28, University of Chieti-Pescara29, University of Ulm30, Genentech31, New York University32, Pennsylvania State University33, University of Salento34, Yale University35, Goethe University Frankfurt36, University of Burgundy37, Pasteur Institute38, University of Strasbourg39, University of Zurich40, University of Tokyo41, Technische Universität München42, University of Bern43, University of Michigan44, Medical Research Council45, University of Adelaide46, University of South Australia47, Medical University of South Carolina48, Howard Hughes Medical Institute49, University of Texas at Dallas50, St. John's University51, University of Oviedo52, University of Graz53, Istituto Superiore di Sanità54, Katholieke Universiteit Leuven55, Trinity College, Dublin56, University of Geneva57, University of Amsterdam58, Stony Brook University59, University of Washington60, University of Ferrara61, Royal College of Surgeons in Ireland62, La Trobe University63, University of Buenos Aires64, University of Virginia65, University of Padua66, University of Lisbon67, University of Cambridge68, University of Würzburg69, Soochow University (Suzhou)70, Columbia University71, University of Glasgow72, University of Crete73, Foundation for Research & Technology – Hellas74, Innsbruck Medical University75, Carlos III Health Institute76, Rutgers University77, University of Minnesota78, Harvard University79, City University of New York80, Moscow State University81
TL;DR: The Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death.
Abstract: Cells exposed to extreme physicochemical or mechanical stimuli die in an uncontrollable manner, as a result of their immediate structural breakdown. Such an unavoidable variant of cellular demise is generally referred to as ‘accidental cell death’ (ACD). In most settings, however, cell death is initiated by a genetically encoded apparatus, correlating with the fact that its course can be altered by pharmacologic or genetic interventions. ‘Regulated cell death’ (RCD) can occur as part of physiologic programs or can be activated once adaptive responses to perturbations of the extracellular or intracellular microenvironment fail. The biochemical phenomena that accompany RCD may be harnessed to classify it into a few subtypes, which often (but not always) exhibit stereotyped morphologic features. Nonetheless, efficiently inhibiting the processes that are commonly thought to cause RCD, such as the activation of executioner caspases in the course of apoptosis, does not exert true cytoprotective effects in the mammalian system, but simply alters the kinetics of cellular demise as it shifts its morphologic and biochemical correlates. Conversely, bona fide cytoprotection can be achieved by inhibiting the transduction of lethal signals in the early phases of the process, when adaptive responses are still operational. Thus, the mechanisms that truly execute RCD may be less understood, less inhibitable and perhaps more homogeneous than previously thought. Here, the Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death.

809 citations

Journal ArticleDOI
TL;DR: Alignment-free metrics are furthering their usage as a scale-independent methodology that is capable of recognizing homology when loss of contiguity is beyond the possibility of alignment.
Abstract: Motivation: Genetic recombination and, in particular, genetic shuffling are at odds with sequence comparison by alignment, which assumes conservation of contiguity between homologous segments. A variety of theoretical foundations are being used to derive alignment-free methods that overcome this limitation. The formulation of alternative metrics for dissimilarity between sequences and their algorithmic implementations are reviewed. Results: The overwhelming majority of work on alignmentfree sequence has taken place in the past two decades, with most reports published in the past 5 years. Two main categories of methods have been proposed—methods based on word (oligomer) frequency, and methods that do not require resolving the sequence with fixed word length segments. The first category is based on the statistics of word frequency, on the distances defined in a Cartesian space defined by the frequency vectors, and on the information content of frequency distribution. The second category includes the use of Kolmogorov complexity and Chaos Theory. Despite their low visibility, alignment-free metrics are in fact already widely used as pre-selection filters for alignment-based querying of large applications. Recent work is furthering their usage as a scale-independent methodology that is capable of recognizing homology when loss of contiguity is beyond the possibility of alignment. Availability: Most of the alignment-free algorithms reviewed were implemented in MATLAB code and are available at http://bioinformatics.musc.edu/resources.html. Contact: almeidaj@musc.edu; svinga@itqb.unl.pt

807 citations


Authors

Showing all 23601 results

NameH-indexPapersCitations
Edward Giovannucci2061671179875
Ronald Klein1941305149140
Peter W.F. Wilson181680139852
Yusuke Nakamura1792076160313
John J.V. McMurray1781389184502
Nora D. Volkow165958107463
L. Joseph Melton16153197861
Gregg C. Fonarow1611676126516
Michael Boehnke152511136681
Charles B. Nemeroff14997990426
Deepak L. Bhatt1491973114652
Clifford R. Jack14096594814
Scott D. Solomon1371145103041
Karl Swedberg136706111214
Charles J. Yeo13667276424
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202364
2022196
20212,654
20202,488
20192,343
20182,094