ArticlePloS one2020
Detection and quantification of renal fibrosis by computerized tomography.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- The Wake Forest Nonhuman Primate Radiation Late Effects Cohort.Radiation research · 2025Article
- Salt-Responsive Gut Microbiota Induces Sex-Specific Blood Pressure Changes.Circulation research · 2024Article
- Hypoxia and collagen deposition in the kidneys infected with Acanthamoeba sp.Scientific reports · 2024Article
- Resilience, aging, and response to radiation exposure (RARRE) in nonhuman primates: a resource review.GeroScience · 2023Review
- Assessment of Blood Pressure in Irradiated Rhesus Macaques (Macaca mulatta).Radiation research · 2023Article
- Evaluation of Renal Fibrosis Using Magnetization Transfer Imaging at 1.5T and 3T in a Porcine Model of Renal Artery Stenosis.Journal of clinical medicine · 2023Article
- Measurement of renal cortical fibrosis by CT scan.Research in diagnostic and interventional imaging · 2023Article
- Automated Computer-Assisted Image Analysis for the Fast Quantification of Kidney Fibrosis.Biology · 2022Article
- Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis.Translational andrology and urology · 2022Article
- Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images.JAMA network open · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesReliable biomarkers for renal fibrosis are needed for clinical care and for research. Existing non-invasive biomarkers are imprecise, which has limited their utility.
methodsWe developed a method to quantify fibrosis by subject size-adjusted CT Hounsfield units. This was accomplished using CT measurements of renal cortex in previously irradiated non-human primates.
resultsRenal cortex mean CT Hounsfield units that were adjusted for body size had a very good direct correlation with renal parenchymal fibrosis, with an area under the curve of 0.93.
conclusionsThis metric is a promising and simple non-invasive biomarker for renal fibrosis.
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Registered trials
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.