Evidence map›Paper›PMID 29541494›Full record

ArticleBiomedical optics express2018

Optical properties of acute kidney injury measured by quantitative phase imaging.

Sungbea Ban, Eunjung Min, Songyee Baek, Hyug Moo Kwon, Gabriel Popescu, Woonggyu Jung

Abstract read
In one paragraph

Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
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  6. Article
  7. Tissue spatial correlation as cancer marker.Journal of biomedical optics · 2019
    Article
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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

6 authors.

Sungbea BanDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Eunjung MinRowland Institute, Harvard University, Boston, Massachusetts, USA.
Songyee BaekDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Hyug Moo KwonDepartment of Biological Science, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Gabriel PopescuQuantitative Light Imaging Laboratory, Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
Woonggyu JungDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diagnosis of acute kidney disease (AKI) has been examined mainly by histology, immunohistochemistry and western blot. Though these approaches are widely accepted in the field, it has an inherent limitation due to the lack of high-throughput and quantitative information. For a better understanding of prognosis in AKI, we present a new approach using quantitative phase imaging combined with a wide-field scanning platform. Through the phase-delay information from the tissue, we were able to predict a stage of AKI based on various optical properties such as light scattering coefficient and anisotropy. These optical parameters quantify the deterioration process of the AKI model of tissue. Our device would be a very useful tool when it is required to deliver fast feedback of tissue pathology or when diseases are related to mechanical properties such as fibrosis.

Indexed as

(110.3080) Quantitative phase imaging(120.5050) Phase measurement(160.4760) Optical properties(180.3170) Interference microscopy(290.5820) Scattering measurements

Identifiers

PMID29541494
PMCPMC5846539

What OpenQuestion holds

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Registered trials

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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.