Article in Cell metabolism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 60% of its field
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.
3 · Its place in the literature
Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
Iain MathiesonDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: mathi@pennmedicine.upenn.edu.
Struan F A GrantDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Divisions of Genetics and Endocrinology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: grants@email.chop.edu.
Children's Hospital of Philadelphia · USUniversity of Pennsylvania · US
Funding
Genome Wide Association Study for Childhood ObesityR01HD056465 · NICHD · CHILDREN'S HOSP OF PHILADELPHIA · PI GRANT, STRUAN F A · 2008 to 2024
$9.5M
Functional Interrogation of T2D-associated genes in human stem cell-derived models and miceUM1DK126194 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI GRANT, STRUAN F A, KAESTNER, KLAUS H · 2020 to 2024
$8.8M
Elucidation of Genetic Effects on Sleep and Circadian TraitsR01HL143790 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI GEHRMAN, PHILIP RICHARD, GRANT, STRUAN F A · 2018 to 2021
$3.6M
Polygenic prediction and evolution of complex traitsR35GM133708 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Iain Neil Mathieson · 2019 to 2026
The need for discovering new genes driving metabolic disease susceptibility is clear; even clearer is the need for their subsequent functional characterization. A new paper reports a role for miR-128-1 in metabolic control through a series of elegant mouse studies, and an intriguing hypothesis about its "thrifty" role in metabolism.
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.
A Selective Sweep Conceals a MicroRNA with Broad Metabolic Effects. · full record | OpenQuestion