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- Kaptoge, S., et al.
(author)
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World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
- 2019
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In: Lancet Global Health. - : Elsevier BV. - 2214-109X. ; 7:10
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Journal article (peer-reviewed)abstract
- Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0.685 (95% CI 0 . 629-0 741) to 0.833 (0 . 783-0- 882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
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- Diego, J. M., et al.
(author)
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JWST's PEARLS : A new lens model for ACT-CL J0102-4915, "El Gordo," and the first red supergiant star at cosmological distances discovered by JWST
- 2023
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 672
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Journal article (peer-reviewed)abstract
- The first James Webb Space Telescope (JWST) data on the massive colliding cluster El Gordo allow for 23 known families of multiply lensed images to be confirmed and for eight new members of these families to be identified. Based on these families, which have been confirmed spectroscopically by MUSE, we derived an initial lens model. This model guided the identification of 37 additional families of multiply lensed galaxies, among which 28 are entirely new systems, and nine were previously known. The initial lens model determined geometric redshifts for the 37 new systems. The geometric redshifts agree reasonably well with spectroscopic or photometric redshifts when those are available. The geometric redshifts enable two additional models that include all 60 families of multiply lensed galaxies spanning a redshift range 2 z z > 0.8 and has an estimated virial mass close the maximum mass allowed by standard cosmological models. The JWST images also reveal the presence of small-mass perturbers that produce small lensing distortions. The smallest of these is consistent with being a dwarf galaxy at z = 0.87 and has an estimated mass of 3.8 x 10(9) M-circle dot, making it the smallest substructure found at z > 0.5. The JWST images also show several candidate caustic-crossing events. One of them is detected at high significance at the expected position of the critical curve and is likely a red supergiant star at z = 2.1878. This would be the first red supergiant found at cosmological distances. The cluster lensing should magnify background objects at z > 6, making more of them visible than in blank fields of a similar size, but there appears to be a deficiency of such objects.
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- Windhorst, Rogier A., et al.
(author)
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JWST PEARLS. Prime extragalactic areas for reionization and lensing science : project overview and first results
- 2023
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In: Astronomical Journal. - : Institute of Physics (IOP). - 0004-6256 .- 1538-3881. ; 165:1
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Journal article (peer-reviewed)abstract
- We give an overview and describe the rationale, methods, and first results from NIRCam images of the JWST “Prime Extragalactic Areas for Reionization and Lensing Science” (PEARLS) project. PEARLS uses up to eight NIRCam filters to survey several prime extragalactic survey areas: two fields at the North Ecliptic Pole (NEP); seven gravitationally lensing clusters; two high redshift protoclusters; and the iconic backlit VV 191 galaxy system to map its dust attenuation. PEARLS also includes NIRISS spectra for one of the NEP fields and NIRSpec spectra of two high-redshift quasars. The main goal of PEARLS is to study the epoch of galaxy assembly, active galactic nucleus (AGN) growth, and First Light. Five fields—the JWST NEP Time-Domain Field (TDF), IRAC Dark Field, and three lensing clusters—will be observed in up to four epochs over a year. The cadence and sensitivity of the imaging data are ideally suited to find faint variable objects such as weak AGN, high-redshift supernovae, and cluster caustic transits. Both NEP fields have sightlines through our Galaxy, providing significant numbers of very faint brown dwarfs whose proper motions can be studied. Observations from the first spoke in the NEP TDF are public. This paper presents our first PEARLS observations, their NIRCam data reduction and analysis, our first object catalogs, the 0.9–4.5 μm galaxy counts and Integrated Galaxy Light. We assess the JWST sky brightness in 13 NIRCam filters, yielding our first constraints to diffuse light at 0.9–4.5 μm. PEARLS is designed to be of lasting benefit to the community.
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- Beger, Richard D., et al.
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Metabolomics enables precision medicine : "A White Paper, Community Perspective"
- 2016
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In: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:10
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Journal article (peer-reviewed)abstract
- INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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