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Margaret R. Karagas

Bio: Margaret R. Karagas is an academic researcher from Dartmouth College. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 84, co-authored 430 publications receiving 24195 citations. Previous affiliations of Margaret R. Karagas include Dartmouth–Hitchcock Medical Center.


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Journal ArticleDOI
TL;DR: In the version of this article initially published online, Simonetta Guarrera and Silvia Polidoro were inadvertently omitted from the author list, and an affiliation was omitted for Paolo Vineis as mentioned in this paper.
Abstract: Nat. Genet. 41, 991–995 (2009); published online 2 August; corrected online 23 August 2009 In the version of this article initially published online, Simonetta Guarrera and Silvia Polidoro were inadvertently omitted from the author list, and an affiliation was omitted for Paolo Vineis. These errors have been corrected in all versions of this article.

1,460 citations

Journal ArticleDOI
TL;DR: This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations.
Abstract: Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P<0.0001) and were significant predictors of tissue origin (P<0.0001). In solid tissues (n = 119) we found striking, highly significant CpG island-dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (P<0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age- and exposure-related differences in tissue-specific methylation and significant age-associated methylation patterns which are CpG island context-dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference and disease-related epigenomes and in the interpretation of potentially pathologically important alterations.

1,005 citations

Journal ArticleDOI
TL;DR: A synthesis of the current knowledge on the human health effects of low-level MeHg exposure to provide a basis for future research efforts, risk assessment, and exposure remediation policies worldwide is undertaken.
Abstract: Background: Methylmercury (MeHg) is a known neuro-toxicant. Emerging evidence indicates it may have adverse effects on the neuro-logic and other body systems at common low levels of exposure. Impac...

558 citations

Journal ArticleDOI
Nathaniel Rothman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Núria Malats, Xifeng Wu1, Jonine D. Figueroa, Francisco X. Real2, David Van Den Berg3, Giuseppe Matullo4, Dalsu Baris, Michael J. Thun5, Lambertus A. Kiemeney6, Paolo Vineis7, Immaculata De Vivo8, Demetrius Albanes, Mark P. Purdue, Thorunn Rafnar9, Michelle A.T. Hildebrandt1, Anne E. Kiltie10, Olivier Cussenot, Klaus Golka, Rajesh Kumar11, Jack A. Taylor12, Jose I. Mayordomo13, Kevin B. Jacobs14, Manolis Kogevinas, Amy Hutchinson14, Zhaoming Wang14, Yi-Ping Fu, Ludmila Prokunina-Olsson, Laurie Burdett14, Meredith Yeager14, William Wheeler, Adonina Tardón15, Consol Serra2, Alfredo Carrato, Reina García-Closas16, Josep Lloreta2, Alison Johnson, Molly Schwenn, Margaret R. Karagas17, Alan R. Schned17, Gerald L. Andriole18, Robert L. Grubb18, Amanda Black, Eric J. Jacobs5, W. Ryan Diver5, Susan M. Gapstur5, Stephanie J. Weinstein, Jarmo Virtamo12, Victoria K. Cortessis3, Manuela Gago-Dominguez3, Malcolm C. Pike3, Malcolm C. Pike19, Mariana C. Stern3, Jian-Min Yuan20, David J. Hunter21, Monica McGrath21, Colin P.N. Dinney1, Bogdan Czerniak1, Meng Chen1, Hushan Yang1, Sita H. Vermeulen6, Katja K.H. Aben6, J. Alfred Witjes6, Remco R. R. Makkinje6, Patrick Sulem9, Søren Besenbacher9, Kari Stefansson9, Kari Stefansson22, Elio Riboli7, Paul Brennan23, Salvatore Panico, Carmen Navarro, Naomi E. Allen24, H. Bas Bueno-de-Mesquita, Dimitrios Trichopoulos21, Dimitrios Trichopoulos25, Neil E. Caporaso, Maria Teresa Landi, Federico Canzian11, Börje Ljungberg26, Anne Tjønneland, Françoise Clavel-Chapelon27, D T Bishop28, Mark Teo28, Margaret A. Knowles28, Simonetta Guarrera, Silvia Polidoro, Fulvio Ricceri4, Carlotta Sacerdote4, Alessandra Allione, Geraldine Cancel-Tassin, Silvia Selinski, Jan G. Hengstler, Holger Dietrich29, Tony Fletcher, Peter Rudnai12, Eugen Gurzau30, Kvetoslava Koppova, Sophia C.E. Bolick12, Ashley C. Godfrey12, Zongli Xu12, José I Sanz-Velez, Maria D. Garcia-Prats, Manuel Sanchez13, Gabriel Valdivia13, Stefano Porru31, Simone Benhamou32, Simone Benhamou33, Robert N. Hoover, Joseph F. Fraumeni, Debra T. Silverman, Stephen J. Chanock 
TL;DR: Two new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1 are identified and previous candidate associations for the GSTM1 deletion and a tag SNP for NAT2 acetylation status are validated, and interactions with smoking in both regions are found.
Abstract: We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q131, 19q12 and 2q371: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q131, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q371 We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p163, 8q2421 and 8q243, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

410 citations

Journal ArticleDOI
TL;DR: Fractures at the hip were the most common, accounting for 38% of the fractures identified, and the proximal humerus, distal radius/ulna, and ankle also were common fracture sites; a pattern of rapidly rising rates with age was seen for fractures of the pelvis, hip, and other parts of the femur among women.
Abstract: Current knowledge regarding the basic epidemiology of fractures is largely limited to a few fracture sites, notably those of the hip and distal forearm. To clarify the patterns of incidence of limb fractures in the elderly, we used data from a 5% sample of the U.S. Medicare population over age 65 years during the years 1986-1990. We identified incident fractures of the proximal humerus, other parts of the humerus, proximal radius/ ulna, shaft of the radius/ulna, distal radius/ulna, pelvis, hip, other parts of the femur, patella, ankle, and other parts of the tibia/fibula from diagnoses and procedures coded on claims for inpatient services, outpatient facility use, and physician services. We used Poisson regression to investigate the relation between demographic factors and fracture risk at these sites. Fractures at the hip were the most common, accounting for 38% of the fractures identified. The proximal humerus, distal radius/ulna, and ankle also were common fracture sites. A pattern of rapidly rising rates with age was seen for fractures of the pelvis, hip, and other parts of the femur among women. Fractures distal to the elbow or knee, however, had, at most, modest increases in incidence with age over 65 years. For each of the fractures studied, women had higher rates than men of the same race, and whites generally had higher rates than blacks of the same gender. Gender-related differences in risk were larger among whites than among blacks, and racial differences in risk were more marked among women than among men.

396 citations


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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 2014
TL;DR: These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care.
Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

9,618 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations