Evidence map›Paper›PMID 29129041›Full record

ArticleJournal of biomedical optics2017

Line-scanning confocal microendoscope for nuclear morphometry imaging.

Yubo Tang, Jennifer Carns, Rebecca R Richards-Kortum

Open access · goldAbstract read
In one paragraph

Article in Journal of biomedical optics, 2017. 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
2.5field-weighted citation impact, top 10% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Yubo TangRice University, Department of Bioengineering, Houston, Texas, United States.
Jennifer CarnsRice University, Department of Bioengineering, Houston, Texas, United States.
Rebecca R Richards-KortumRice University, Department of Bioengineering, Houston, Texas, United States.
Rice University · US

Funding

Optical Systems for In Vivo Molecular Imaging of CancerR01CA103830 · NCI · UNIVERSITY OF TEXAS AUSTIN · PI RICHARDS-KORTUM, REBECCA R. · 2004 to 2015
$12.4M
Academic-Industrial Partnership to Develop & Test Esophageal Cancer Imaging ToolsR01CA140257 · NCI · RICE UNIVERSITY · PI ANANDASABAPATHY, SHARMILA, CALDER, GREGORY A · 2010 to 2014
$3.4M
NCI NIH HHS R01 CA103830NCI NIH HHS R01 CA140257
6 · The paper itself

Abstract

Fiber-optic endomicroscopy is a minimally invasive method to image cellular morphology in vivo. Using a coherent fiber bundle as an image relay, it allows additional imaging optics to be placed at the distal end of the fiber outside the body. In this research, we use this approach to demonstrate a compact, low-cost line-scanning confocal fluorescence microendoscope that can be constructed for <$5000. Confocal imaging is enabled without the need for mechanical scanning by synchronizing a digital light projector with the rolling shutter of a CMOS camera. Its axial performance is characterized in comparison with a nonscanned high-resolution microendoscope. We validate the optical sectioning capability of the microendoscope by imaging a two-dimensional phantom and ex vivo mouse esophageal and colon tissues. Results show that optical sectioning using this approach improves visualization of nuclear morphometry and suggest that this low-cost line-scanning microendoscope can be used to evaluate various pathological conditions.

Indexed as

AnimalsCell NucleusColonEndoscopyEsophagusMiceOptics and PhotonicsCMOS rolling shutterdigital light projectorline-scanninglow-costmicroendoscopeoptical sectioning

Identifiers

PMID29129041
PMCPMC5681860
OpenAlexW2767803690

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.