Review in Arteriosclerosis, thrombosis, and vascular biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
4 authors.
Hanrui ZhangFrom the Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York, NY (H.Z.); Division of Cardiology, School of Medicine, University of California Los Angeles (T.Q. de A. V.); and Department of Medicine, Montreal Heart Institute Research Center, Université de Montréal, Montreal, Quebec, Canada (C.M.). hz2418@cumc.columbia.edu tvallim@mednet.ucla.edu catherine.martel@icm-mhi.org.
Thomas Q de Aguiar VallimFrom the Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York, NY (H.Z.); Division of Cardiology, School of Medicine, University of California Los Angeles (T.Q. de A. V.); and Department of Medicine, Montreal Heart Institute Research Center, Université de Montréal, Montreal, Quebec, Canada (C.M.). hz2418@cumc.columbia.edu tvallim@mednet.ucla.edu catherine.martel@icm-mhi.org.
Catherine MartelFrom the Division of Cardiology, Department of Medicine, Columbia University Medical Center, New York, NY (H.Z.); Division of Cardiology, School of Medicine, University of California Los Angeles (T.Q. de A. V.); and Department of Medicine, Montreal Heart Institute Research Center, Université de Montréal, Montreal, Quebec, Canada (C.M.). hz2418@cumc.columbia.edu tvallim@mednet.ucla.edu catherine.martel@icm-mhi.org.
Early Career Committee
Funding
UCLA Clinical and Translational Science InstituteUL1TR000124 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$57.0M
Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REUE, KAREN · 1985 to 2019
$50.2M
Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PETER J TONTONOZ · 2003 to 2026
$40.4M
Transcriptional control of microRNAs in lipid metabolism and atherosclerosisR01HL122677 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VALLIM, THOMAS A · 2016 to 2020
$1.9M
Novel Post-Transcriptional Regulators of Lipid MetabolismR01DK112119 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VALLIM, THOMAS A · 2017 to 2020
$1.7M
Novel transcriptional regulators of bile acid metabolism and hepatotoxicityR01DK102559 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EDWARDS, PETER A · 2014 to 2017
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Translational and Therapeutic Approaches to the Understanding and Treatment of Dyslipidemia. · full record | OpenQuestion