Evidence map›Paper›PMID 20363779›Full record

ArticleApplied and environmental microbiology2010

Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.

Rabih E Jabbour, Samir V Deshpande, Mary Margaret Wade, Michael F Stanford, Charles H Wick, Alan W Zulich, Evan W Skowronski, A Peter Snyder

Abstract readEvaluation Study
In one paragraph

Article in Applied and environmental microbiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

8 authors.

Rabih E JabbourEdgewood Area, Gunpowder Branch, Science Applications International Corporation, Aberdeen Proving Ground, MD 21010-5424, USA. rabih.e.jabbour@saic.com
Samir V Deshpande
Mary Margaret Wade
Michael F Stanford
Charles H Wick
Alan W Zulich
Evan W Skowronski
A Peter Snyder

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.

Indexed as

BacteriaBacterial ProteinsComputational BiologyDouble-Blind MethodMass SpectrometryProteomicsSensitivity and SpecificityTrypsinBacterial ProteinsTrypsin

Identifiers

PMID20363779
PMCPMC2876470

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.