Evidence map›Paper›PMID 33727586›Full record

ArticleScientific reports2021

Full pathogen characterisation: species identification including the detection of virulence factors and antibiotic resistance genes via multiplex DNA-assays.

Noa Wolff, Michaela Hendling, Fabian Schroeder, Silvia Schönthaler, Andreas F Geiss, Branka Bedenic, Ivan Barišić

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

7 authors.

Noa WolffMolecular Diagnostics, AIT Austrian Institute of Technology, Giefinggasse 4, 1210, Vienna, Austria. Noawolff@outlook.de.
Michaela HendlingMolecular Diagnostics, AIT Austrian Institute of Technology, Giefinggasse 4, 1210, Vienna, Austria.
Fabian SchroederEkorefugium, Slunj, Croatia.
Silvia SchönthalerMolecular Diagnostics, AIT Austrian Institute of Technology, Giefinggasse 4, 1210, Vienna, Austria.
Andreas F GeissUniversity of Natural Resources and Life Sciences, Vienna, Austria.
Branka BedenicDepartment of Microbiology, School of Medicine, University of Zagreb, Zagreb, Croatia.
Ivan BarišićMolecular Diagnostics, AIT Austrian Institute of Technology, Giefinggasse 4, 1210, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibiotic resistances progressively cause treatment failures, and their spreading dynamics reached an alarming level. Some strains have already been classified as highly critical, e.g. the ones summarised by the acronym ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.). To restrain this trend and enable effective medication, as much information as possible must be obtained in the least possible time. Here, we present a DNA microarray-based assay that screens for the most important sepsis-relevant 44 pathogenic species, 360 virulence factors (mediate pathogenicity in otherwise non-pathogenic strains), and 409 antibiotic resistance genes in parallel. The assay was evaluated with 14 multidrug resistant strains, including all ESKAPE pathogens, mainly obtained from clinical isolates. We used a cost-efficient ligation-based detection platform designed to emulate the highly specific multiplex detection of padlock probes. Results could be obtained within one day, requiring approximately 4 h for amplification, application to the microarray, and detection.

Indexed as

BacteriaBacterial Typing TechniquesOligonucleotide Array Sequence AnalysisSepsisDrug Resistance, BacterialHumansVirulence FactorsVirulence Factors

Identifiers

PMID33727586
PMCPMC7966752

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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.