Evidence map›Paper›PMID 30367087›Full record

ArticleScientific reports2018

Pathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipids.

William E Fondrie, Tao Liang, Benjamin L Oyler, Lisa M Leung, Robert K Ernst, Dudley K Strickland, David R Goodlett

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

14 citing papers in PubMed, 26 citations in OpenAlex.

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  7. Article
  8. Review
  9. Article
  10. MGMS2: Membrane glycolipid mass spectrum simulator for polymicrobial samples.Rapid communications in mass spectrometry : RCM · 2020
    Article
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  13. Review
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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

7 authors at 1 institution in 1 country.

William E FondrieCenter for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.ORCID http://orcid.org/0000-0002-1554-3716
Tao LiangDepartment of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA.
Benjamin L OylerToxicology and Pharmacology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.ORCID http://orcid.org/0000-0003-3436-9175
Lisa M LeungDepartment of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, 21201, USA.
Robert K ErnstDepartment of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, 21201, USA.
Dudley K StricklandCenter for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
David R GoodlettDepartment of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA. dgoodlett@rx.umaryland.edu.
University of Maryland, Baltimore · US

Funding

Vascular Biology Training ProgramT32HL007698 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI Rosemary A Kozar, Dudley K. Strickland · 1991 to 2026
$8.7M
Role of LDL receptor family members in protecting the vasculatureR35HL135743 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI STRICKLAND, DUDLEY K. · 2017 to 2023
$5.4M
MS diagnostic bacterial identification libraryR01GM111066 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI ERNST, ROBERT K, GOODLETT, DAVID ROBINSON · 2014 to 2017
$1.2M
Defining a mechanism of LRP1B tumor suppression in glioblastomaF31CA213815 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI FONDRIE, WILLIAM ELLIS · 2017 to 2018
$36k
NCI NIH HHS F31 CA213815NHLBI NIH HHS R35 HL135743NHLBI NIH HHS T32 HL007698NIGMS NIH HHS R01 GM111066
6 · The paper itself

Abstract

With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.

Indexed as

Machine LearningAcinetobacter baumanniiAnti-Bacterial AgentsArea Under CurveColistinDatabases, FactualDrug Resistance, Multiple, BacterialGlycolipidsHumansKlebsiella pneumoniaeMass SpectrometryROC CurveAnti-Bacterial AgentsColistinGlycolipids

Identifiers

PMID30367087
PMCPMC6203844
OpenAlexW2897624826

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.