Evidence map›Paper›PMID 29765060›Full record

ArticleScientific reports2018

Iron-specific Signal Separation from within Heavy Metal Stained Biological Samples Using X-Ray Microtomography with Polychromatic Source and Energy-Integrating Detectors.

Tsvi Katchalski, Tom Case, Keun-Young Kim, Ranjan Ramachandra, Eric A Bushong, Thomas J Deerinck, Matthias G Haberl, Mason R Mackey, Steven Peltier, Guillaume A Castillon and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 19% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

13 authors at 2 institutions in 1 country.

Tsvi KatchalskiNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA. tkatchalski@ucsd.edu.
Tom CaseCarl Zeiss X-Ray Microscopy, 4385 Hopyard Road, Suite 100, Pleasanton, CA, 94588, USA.
Keun-Young KimNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Ranjan RamachandraNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Eric A BushongNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.ORCID 0000-0001-6195-2433
Thomas J DeerinckNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Matthias G HaberlNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.ORCID 0000-0002-4961-7832
Mason R MackeyNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Steven PeltierNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Guillaume A CastillonNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Nobuko FujikawaCarl Zeiss X-Ray Microscopy, 4385 Hopyard Road, Suite 100, Pleasanton, CA, 94588, USA.
Albert R LawrenceNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA.
Mark H EllismanNational Center for Microscopy and Imaging Research (NCMIR), University of California San Diego, 9500 Gilman Dr. MC 0608, La Jolla, CA, 92093-0608, USA. mellisman@ucsd.edu.
University of California San Diego · USCarl Zeiss (United States) · US

Funding

IVEM and Image Analysis ResourceP41GM103412 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELLISMAN, MARK H · 2012 to 2019
$17.9M
NIGMS NIH HHS P41 GM103412
6 · The paper itself

Abstract

Biological samples are frequently stained with heavy metals in preparation for examining the macro, micro and ultra-structure using X-ray microtomography and electron microscopy. A single X-ray microtomography scan reveals detailed 3D structure based on staining density, yet it lacks both material composition and functional information. Using a commercially available polychromatic X-ray source, energy integrating detectors and a two-scan configuration labelled by their energy- "High" and "Low", we demonstrate how a specific element, here shown with iron, can be detected from a mixture with other heavy metals. With proper selection of scan configuration, achieving strong overlap of source characteristic emission lines and iron K-edge absorption, iron absorption was enhanced enabling K-edge imaging. Specifically, iron images were obtained by scatter plot material analysis, after selecting specific regions within scatter plots generated from the "High" and "Low" scans. Using this method, we identified iron rich regions associated with an iron staining reaction that marks the nodes of Ranvier along nerve axons within mouse spinal roots, also stained with osmium metal commonly used for electron microscopy.

Indexed as

AnimalsAxonsIronMetals, HeavyMicePhantoms, ImagingSpinal Nerve RootsStaining and LabelingX-Ray MicrotomographyIronMetals, Heavy

Identifiers

PMID29765060
PMCPMC5953933
OpenAlexW2806338907

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.