ArticleJournal of magnetic resonance imaging : JMRI2020
Quantitative Magnetization Transfer Detects Renal Fibrosis in Murine Kidneys With Renal Artery Stenosis.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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
3 citing papers in PubMed, 11 citations in OpenAlex.
- ASL, DTI, and T1 mapping in identifying immune and non-immune pathogenic factors of chronic allograft injury.Quantitative imaging in medicine and surgery · 2026Article
- Advances in imaging techniques to assess kidney fibrosis.Renal failure · 2023Review
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRenal fibrosis is a common pathway in tubulointerstitial injury and a major determinant of renal insufficiency. Collagen deposition, a key feature of renal fibrosis, may serve as an imaging biomarker to differentiate scarred from healthy kidneys. PURPOSE: To test the feasibility of using quantitative magnetization transfer (qMT), which assesses tissue macromolecule content, to measure renal fibrosis. STUDY TYPE: Prospective. ANIMAL MODEL: Fifteen 129S1 mice were studied 4 weeks after either sham (n = 7) or unilateral renal artery stenosis (RAS, n = 8) surgeries. FIELD STRENGTH/SEQUENCE: Magnetization transfer (MT)-weighted images were acquired at 16.4T using an MT-prepared fast-low-angle-shot sequence. Renal B ASSESSMENT: A two-pool model was used to estimate the bound water fraction (f) and other tissue imaging biomarkers. Masson's trichrome staining was subsequently performed ex vivo to evaluate renal fibrosis. STATISTICAL TESTS: Comparisons of renal parameters between sham and RAS were performed using independent samples t-tests. Pearson's correlation was conducted to investigate the relationship between renal fibrosis by histology and the qMT-derived bound pool fraction f.
resultsThe two-pool model provided accurate fittings of measured MT signal. The qMT-derived f of RAS kidneys was significantly increased compared to sham in all kidney zones (renal cortex [CO], 7.6 ± 2.4% vs. 4.6 ± 0.6%; outer medulla [OM], 8.2 ± 4.2% vs. 4.2 ± 0.9%; inner medulla [IM] + P, 5.8 ± 1.6% vs. 2.9 ± 0.6%, all P < 0.05). Measured f correlated well with histological fibrosis in all kidney zones (CO, Pearson's correlation coefficient r = 0.95; OM, r = 0.93; IM + P, r = 0.94, all P < 0.05). DATA
conclusionThe bound pool fraction f can be quantified using qMT at 16.4T in murine kidneys, increases significantly in fibrotic RAS kidneys, and correlates well with fibrosis by histology. Therefore, qMT may constitute a valuable tool for measuring renal fibrosis in RAS. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 3.
Indexed as
Identifiers
What OpenQuestion holds
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.