Evidence map›Paper›PMID 26508443›Full record

ArticleJournal of the American Society for Mass Spectrometry2016

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features.

Chanchala D Kaddi, Rachel V Bennett, Martin R L Paine, Mitchel D Banks, Arthur L Weber, Facundo M Fernández, May D Wang

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2016. 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
–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

2 citing papers in PubMed.

  1. Article
  2. -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.IEEE transactions on bio-medical engineering · 2017
    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.

Chanchala D KaddiWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Rachel V BennettSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Martin R L PaineSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Mitchel D BanksSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Arthur L WeberSETI Institute, Ames Research Center, Moffett Field, CA, 94035, USA.
Facundo M FernándezSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA. facundo.fernandez@chemistry.gatech.edu.
May D WangWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. maywang@bme.gatech.edu.

Funding

Tissue and Animal UseU54CA119338 · NCI · EMORY UNIVERSITY · PI SIMONS, JONATHAN W. · 2005 to 2009
$18.4M
Nanotechnology Linking Biomarkers with Cancer BehaviorR01CA108468 · NCI · EMORY UNIVERSITY · PI NIE, SHUMING · 2004 to 2008
$6.9M
NCI NIH HHS R01 CA108468NCI NIH HHS R01CA108468NCI NIH HHS U54 CA119338NCI NIH HHS U54CA119338
6 · The paper itself

Abstract

Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis. Graphical Abstract ᅟ.

Indexed as

Chromatography, Thin LayerComplex MixturesData MiningFluorescenceImage Processing, Computer-AssistedMass SpectrometryPyrazines2-aminopyrazineComplex MixturesPyrazinesAmbient MSData processingDESI MSFeature detectionImaging mass spectrometry

Identifiers

PMID26508443
PMCPMC5003040

What OpenQuestion holds

Textmetadata
LicenceTDM
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.