Evidence map›Paper›PMID 24647716›Full record

ArticleAmerican journal of physiology. Renal physiology2014

MRI-based glomerular morphology and pathology in whole human kidneys.

Scott C Beeman, Luise A Cullen-McEwen, Victor G Puelles, Min Zhang, Teresa Wu, Edwin J Baldelomar, John Dowling, Jennifer R Charlton, Michael S Forbes, Amanda Ng and 5 more

Abstract read
In one paragraph

Article in American journal of physiology. Renal physiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed
22.3field-weighted citation impact, top 1% 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

67 citing papers in PubMed, 99 citations in OpenAlex.

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  14. Chronic Kidney Disease Induces Proarrhythmic Remodeling.Circulation. Arrhythmia and electrophysiology · 2023
    Article
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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

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

15 authors at 6 institutions in 2 countries.

Scott C BeemanDepartment of Radiology, Washington University School of Medicine, Saint Louis, Missouri;
Luise A Cullen-McEwenDepartment of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria, Australia;
Victor G PuellesDepartment of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria, Australia;
Min ZhangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona;
Teresa WuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona;
Edwin J BaldelomarDepartment of Physics, College of Natural Sciences, University of Hawaii at Manoa, Honolulu, Hawaii;
John DowlingDepartment of Anatomical Pathology, Monash Medical Centre, Clayton, Victoria, Australia;
Jennifer R CharltonDepartment of Pediatrics, Division of Nephrology, University of Virginia Medical Center, Charlottesville, Virginia;
Michael S ForbesDepartment of Pediatrics, Division of Nephrology, University of Virginia Medical Center, Charlottesville, Virginia;
Amanda NgMonash Biomedical Imaging, Monash University, Melbourne, Australia; and.
Qi-zhu WuMonash Biomedical Imaging, Monash University, Melbourne, Australia; and.
James A ArmitageDepartment of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria, Australia;
Gary F EganMonash Biomedical Imaging, Monash University, Melbourne, Australia; and.
John F BertramDepartment of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria, Australia;
Kevin M BennettDepartment of Biology, College of Natural Sciences, University of Hawaii at Manoa, Honolulu, Hawaii kevinben@hawaii.edu.
Monash University · AUArizona State University · USUniversity of Hawaiʻi at Mānoa · USUniversity of Virginia Medical Center · USMonash Medical Centre · AUWashington University in St. Louis · US

Funding

MRI Techniques to Measure Whole-Kidney NephronR21DK091722 · NIDDK · UNIVERSITY OF HAWAII AT MANOA · PI BENNETT, KEVIN M · 2012 to 2013
$399k
NIDDK NIH HHS DK-091722
6 · The paper itself

Abstract

Nephron number (N(glom)) and size (V(glom)) are correlated with risk for chronic cardiovascular and kidney disease and may be predictive of renal allograft viability. Unfortunately, there are no techniques to assess N(glom) and V(glom) in intact kidneys. This work demonstrates the use of cationized ferritin (CF) as a magnetic resonance imaging (MRI) contrast agent to measure N(glom) and V(glom) in viable human kidneys donated to science. The kidneys were obtained from patients with varying levels of cardiovascular and renal disease. CF was intravenously injected into three viable human kidneys. A fourth control kidney was perfused with saline. After fixation, immunofluorescence and electron microscopy confirmed binding of CF to the glomerulus. The intact kidneys were imaged with three-dimensional MRI and CF-labeled glomeruli appeared as punctate spots. Custom software identified, counted, and measured the apparent volumes of CF-labeled glomeruli, with an ~6% false positive rate. These measurements were comparable to stereological estimates. The MRI-based technique yielded a novel whole kidney distribution of glomerular volumes. Histopathology demonstrated that the distribution of CF-labeled glomeruli may be predictive of glomerular and vascular disease. Variations in CF distribution were quantified using image texture analyses, which be a useful marker of glomerular sclerosis. This is the first report of direct measurement of glomerular number and volume in intact human kidneys.

Indexed as

AdultAgedFemaleHumansKidneyKidney DiseasesKidney GlomerulusMagnetic Resonance ImagingMaleMiddle Agedcationized ferritinchronic kidney diseasemagnetic resonance imaging

Identifiers

PMID24647716
PMCPMC4042103
OpenAlexW2038472491

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.