Evidence map›Paper›PMID 34675323›Full record

ArticleScientific reports2021

Diffusion tensor imaging for the study of early renal dysfunction in patients affected by bardet-biedl syndrome.

Pasquale Borrelli, Miriam Zacchia, Carlo Cavaliere, Luca Basso, Marco Salvatore, Giovambattista Capasso, Marco Aiello

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 28% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Diffusion tensor imaging of the healthy feline kidney.Journal of feline medicine and surgery · 2026
    Article
  2. Article
  3. Obesity-Related Ciliopathies: Focus on Advances of Biomarkers.International journal of molecular sciences · 2024
    Review
  4. Bardet-Biedl Syndrome: Current Perspectives and Clinical Outlook.Therapeutics and clinical risk management · 2023
    Review
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

7 authors at 4 institutions in 1 country.

Pasquale BorrelliIRCCS SDN, Via Emanuele Gianturco 113, 80131, Naples, Italy. pasquale.borrelli@synlab.it.
Miriam ZacchiaDepartment of Medical and Translational Sciences, University of Campania L. Vanvitelli, Naples, Italy.
Carlo CavaliereIRCCS SDN, Via Emanuele Gianturco 113, 80131, Naples, Italy.
Luca BassoIRCCS SDN, Via Emanuele Gianturco 113, 80131, Naples, Italy.
Marco SalvatoreIRCCS SDN, Via Emanuele Gianturco 113, 80131, Naples, Italy.
Giovambattista CapassoDepartment of Medical and Translational Sciences, University of Campania L. Vanvitelli, Naples, Italy.
Marco AielloIRCCS SDN, Via Emanuele Gianturco 113, 80131, Naples, Italy.
Istituti di Ricovero e Cura a Carattere Scientifico · ITBiogem · ITSDN Istituto di Ricerca Diagnostica e Nucleare · ITUniversity of Campania "Luigi Vanvitelli" · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney structural abnormalities are common features of Bardet-Biedl syndrome (BBS) patients that lead to a progressive decline in renal function. Magnetic resonance diffusion tensor imaging (DTI) provides useful information on renal microstructures but it has not been applied to these patients. This study investigated using DTI to detect renal abnormalities in BBS patients with no overt renal dysfunction. Ten BBS subjects with estimated glomerular filtration rates over 60 ml/min/1.73m

Indexed as

AdultBardet-Biedl SyndromeDiffusion Tensor ImagingFemaleGlomerular Filtration RateHumansKidneyMaleRenal Insufficiency, ChronicYoung Adult

Identifiers

PMID34675323
PMCPMC8531379
OpenAlexW3206258304

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.