Next-Generation Sequencing of Infectious Pathogens Gwinn, Marta; MacCannell, Duncan; Armstrong, Gregory L
JAMA : the journal of the American Medical Association,
2019-Mar-05, Letnik:
321, Številka:
9
Journal Article
In this paper, we review the evolution of the field of public health genomics in the United States in the past two decades. Public health genomics focuses on effective and responsible translation of ...genomic science into population health benefits. We discuss the relationship of the field to the core public health functions and essential services, review its evidentiary foundation, and provide examples of current US public health priorities and applications. We cite examples of publications to illustrate how Genetics in Medicine reflected the evolution of the field. We also reflect on how public-health genomics is contributing to the emergence of "precision public health" with near-term opportunities offered by the US Precision Medicine (AllofUs) Initiative.
We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons ...with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population.
We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories.
For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold.
Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.
A Population Approach to Precision Medicine Khoury, Muin J., MD, PhD; Gwinn, Marta L., MD, MS; Glasgow, Russell E., PhD ...
American journal of preventive medicine,
06/2012, Letnik:
42, Številka:
6
Journal Article
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Abstract The term P4 medicine is used to denote an evolving field of medicine that uses systems biology approaches and information technologies to enhance wellness rather than just treat disease. Its ...four components include predictive, preventive, personalized, and participatory medicine. In the current paper, it is argued that in order to fulfill the promise of P4 medicine, a “fifth P” must be integrated—the population perspective—into each of the other four components. A population perspective integrates predictive medicine into the ecologic model of health; applies principles of population screening to preventive medicine; uses evidence-based practice to personalize medicine; and grounds participatory medicine on the three core functions of public health: assessment, policy development, and assurance. Population sciences—including epidemiology; behavioral, social, and communication sciences; and health economics, implementation science, and outcomes research—are needed to show the value of P4 medicine. Balanced strategies that implement both population- and individual-level interventions can best maximize health benefits, minimize harm, and avoid unnecessary healthcare costs.
We recently developed CoCites, a citation-based search method that is designed to be more efficient than traditional keyword-based methods. The method begins with identification of one or more highly ...relevant publications (query articles) and consists of two searches: the co-citation search, which ranks publications on their co-citation frequency with the query articles, and the citation search, which ranks publications on frequency of all citations that cite or are cited by the query articles.
We aimed to reproduce the literature searches of published systematic reviews and meta-analyses and assess whether CoCites retrieves all eligible articles while screening fewer titles.
A total of 250 reviews were included. CoCites retrieved a median of 75% of the articles that were included in the original reviews. The percentage of retrieved articles was higher (88%) when the query articles were cited more frequently and when they had more overlap in their citations. Applying CoCites to only the highest-cited article yielded similar results. The co-citation and citation searches combined were more efficient when the review authors had screened more than 500 titles, but not when they had screened less.
CoCites is an efficient and accurate method for finding relevant related articles. The method uses the expert knowledge of authors to rank related articles, does not depend on keyword selection and requires no special expertise to build search queries. The method is transparent and reproducible.
The scientific response to the COVID-19 pandemic has produced an abundance of publications, including peer-reviewed articles and preprints, across a wide array of disciplines, from microbiology to ...medicine and social sciences. Genomics and precision health (GPH) technologies have had a particularly prominent role in medical and public health investigations and response; however, these domains are not simply defined and it is difficult to search for relevant information using traditional strategies. To quantify and track the ongoing contributions of GPH to the COVID-19 response, the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention created the COVID-19 Genomics and Precision Health database (COVID-19 GPH), an open access knowledge management system and publications database that is continuously updated through machine learning and manual curation. As of February 11, 2022, COVID-GPH contained 31,597 articles, mostly on pathogen and human genomics (72%). The database also includes articles describing applications of machine learning and artificial intelligence to the investigation and control of COVID-19 (28%). COVID-GPH represents about 10% (22983/221241) of the literature on COVID-19 on PubMed. This unique knowledge management database makes it easier to explore, describe, and track how the pandemic response is accelerating the applications of genomics and precision health technologies. COVID-19 GPH can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action .
Preprints have had a prominent role in the swift scientific response to COVID-19. Two years into the pandemic, we investigated how much preprints had contributed to timely data sharing by analyzing ...the lag time from preprint posting to journal publication. To estimate the median number of days between the date a manuscript was posted as a preprint and the date of its publication in a scientific journal, we analyzed preprints posted from January 1, 2020, to December 31, 2021 in the NIH iSearch COVID-19 Portfolio database and performed a Kaplan-Meier (KM) survival analysis using a non-mixture parametric cure model. Of the 39,243 preprints in our analysis, 7712 (20%) were published in a journal, after a median lag of 178 days (95% CI: 175-181). Most of the published preprints were posted on the bioRxiv (29%) or medRxiv (65%) servers, which allow authors to choose a subject category when posting. Of the 20,698 preprints posted on these two servers, 7358 (36%) were published, including approximately half of those categorized as biochemistry, biophysics, and genomics, which became published articles within the study interval, compared with 29% categorized as epidemiology and 26% as bioinformatics.
About the Authors: A. Cecile J. W. Janssens Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Visualization, Writing - original draft, Writing - review & editing * E-mail: ...cecile.janssens@emory.edu Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America Michael Goodman Roles Writing - review & editing Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America Kimberly R. Powell Roles Investigation, Methodology, Writing - review & editing Affiliation: Woodruff Health Sciences Center Library, Emory University, Atlanta, Georgia, United States of America Marta Gwinn Roles Writing - review & editing Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of AmericaCitation: Janssens ACJW, Goodman M, Powell KR, Gwinn M (2017) A critical evaluation of the algorithm behind the Relative Citation Ratio (RCR). Abbreviations: ACR, article citation rate; FCR, field citation rate; JCR, journal citation rate; JIF, journal impact factor; NIH, National Institutes of Health; RCR, Relative Citation Ratio Provenance: Not commissioned; externally peer reviewed. Research articles generally lose their relevance when science progresses, and after a while, they may no longer be cited 6. Because the number of years since publication continues to increase, the ACR will inevitably decline, and so will the RCR. ...the RCR is normalized against a collection of 311,497 publications from NIH projects to obtain a benchmark against which articles can be compared.
Prenatal stress and poor maternal mental health are associated with adverse offspring outcomes; however, the biological mechanisms are unknown. Epigenetic modification has linked maternal health with ...offspring development. Epigenome-wide association studies (EWAS) have examined offspring DNA methylation profiles for association with prenatal maternal mental health to elucidate mechanisms of these complex relationships. The objective of this study is to provide a comprehensive, systematic review of EWASs of infant epigenetic profiles and prenatal maternal anxiety, depression, or depression treatment. We conducted a systematic literature search following PRISMA guidelines for EWAS studies between prenatal maternal mental health and infant epigenetics through May 22, 2023. Of 645 identified articles, 20 fulfilled inclusion criteria. We assessed replication of CpG sites among studies, conducted gene enrichment analysis, and evaluated the articles for quality and risk of bias. We found one repeated CpG site among the maternal depression studies; however, nine pairs of overlapping differentially methylatd regions were reported in at least two maternal depression studies. Gene enrichment analysis found significant pathways for maternal depression but not for any other maternal mental health category. We found evidence that these EWAS present a medium to high risk of bias. Exposure to prenatal maternal depression and anxiety or treatment for such was not consistently associated with epigenetic changes in infants in this systematic review and meta-analysis. Small sample size, potential bias due to exposure misclassification and statistical challenges are critical to address in future efforts to explore epigenetic modification as a potential mechanism by which prenatal exposure to maternal mental health disorders leads to adverse infant outcomes.
Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine ...and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.
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