Evidence map›Paper›PMID 26537565›Full record

ArticleScientific reports2015

Biomarker- and similarity coefficient-based approaches to bacterial mixture characterization using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Lin Zhang, Sonja Smart, Todd R Sandrin

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

  1. Evaluating the Autof MS2600 vs. Biotyper Sirius MALDI-TOF MS: a head-to-head study for routine microbial identification in a university clinical microbiology laboratory.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
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  8. Clinical Mass Spectrometry in the Bioinformatics Era: A Hitchhiker's Guide.Computational and structural biotechnology journal · 2018
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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.

Lin ZhangSchool of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069.
Sonja SmartSchool of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069.
Todd R SandrinSchool of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MALDI-TOF MS profiling has been shown to be a rapid and reliable method to characterize pure cultures of bacteria. Currently, there is keen interest in using this technique to identify bacteria in mixtures. Promising results have been reported with two- or three-isolate model systems using biomarker-based approaches. In this work, we applied MALDI-TOF MS-based methods to a more complex model mixture containing six bacteria. We employed: 1) a biomarker-based approach that has previously been shown to be useful in identification of individual bacteria in pure cultures and simple mixtures and 2) a similarity coefficient-based approach that is routinely and nearly exclusively applied to identification of individual bacteria in pure cultures. Both strategies were developed and evaluated using blind-coded mixtures. With regard to the biomarker-based approach, results showed that most peaks in mixture spectra could be assigned to those found in spectra of each component bacterium; however, peaks shared by two isolates as well as peaks that could not be assigned to any individual component isolate were observed. For two-isolate blind-coded samples, bacteria were correctly identified using both similarity coefficient- and biomarker-based strategies, while for blind-coded samples containing more than two isolates, bacteria were more effectively identified using a biomarker-based strategy.

Indexed as

BacteriaBiomarkersSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBiomarkers

Identifiers

PMID26537565
PMCPMC4633581

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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.