Evidence map›Paper›PMID 29708187›Full record

ArticleEuropean radiology experimental2017

Investigation of aquaporins and apparent diffusion coefficient from ultra-high b-values in a rat model of diabetic nephropathy.

Yu Wang, Heng Zhang, Ruzhi Zhang, Zhoushe Zhao, Ziqian Xu, Lei Wang, Rongbo Liu, Fabao Gao

Abstract read
In one paragraph

Article in European radiology experimental, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Based on Histogram Analysis: ADCScientific reports · 2020
    Article
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

8 authors.

Yu Wang1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Heng Zhang1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Ruzhi Zhang1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Zhoushe ZhaoGeneral Electronic Company Healthcare (China), Beijing, China.
Ziqian Xu1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Lei Wang1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Rongbo Liu1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.
Fabao Gao1Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Lane outside the south, Wuhou District Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo assess kidney damage in a rat model of type-2 diabetic nephropathy based on apparent diffusion coefficient (ADC) data obtained from ultra-high b-values and discuss its relationship to the expression of aquaporins (AQPs).

methodsThis study was approved by the institutional Animal Care and Use Committee. Thirty male Sprague-Dawley rats were randomised into two groups: (1) untreated controls and (2) diabetes mellitus (DM). All rats underwent diffusion-weighted imaging (DWI) with 18 b-values (0-4500 s/mm

resultsADC

conclusionsADC

Indexed as

Apparent diffusion coefficient (ADC)AquaporinsDiabetic nephropathyDiffusion-weighted imaging (DWI)Ultra-high b-values

Identifiers

PMID29708187
PMCPMC5909346

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.