Evidence map›Paper›PMID 23296330›Full record

ArticleSensors (Basel, Switzerland)2013

Detection of Aeromonas hydrophila in liquid media by volatile production similarity patterns, using a FF-2A electronic nose.

Kouki Fujioka, Eiji Arakawa, Jun-ichi Kita, Yoshihiro Aoyama, Yoshinobu Manome, Keiichi Ikeda, Kenji Yamamoto

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

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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 4 institutions in 1 country.

Kouki FujiokaDepartment of Molecular Cell Biology, Institute of DNA Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo, Japan. kfujioka@jikei.ac.jp
Eiji Arakawa
Jun-ichi Kita
Yoshihiro Aoyama
Yoshinobu Manome
Keiichi Ikeda
Kenji Yamamoto
Jikei University School of Medicine · JPShimadzu (Japan) · JPNational Center for Global Health and Medicine · JPNational Institute of Infectious Diseases · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A technique for rapid detection of pathogenic microorganisms is essential for the diagnosis of associated infections and for food safety analysis. Aeromonas hydrophila is one such food contaminant. Several methods for rapid detection of this pathogen have been developed; these include multiplex polymerase chain reaction assays and the colony overlay procedure for peptidases. However, these conventional methods can only be used to detect the microorganisms at high accuracy after symptomatic onset of the disease. Therefore, in the future, simple pre-screening methods may be useful for preventing food poisoning and disease. In this paper, we present a novel system for the rapid detection of the microorganism A. hydrophila in cultured media (in <2 h), with the use of an electronic nose (FF-2A). With this electronic nose, we detected the changes of volatile patterns produced by A. hydrophila after 30 min culture. Our calculations revealed that the increased volatiles were similar to the odours of organic acids and esters. In future, distinctive volatile production patterns of microorganisms identified with the electronic nose may have the potential in microorganism detection.

Indexed as

Electronic NoseAeromonas hydrophilaAlgorithmsCulture MediaGasesTime FactorsVolatilizationCulture MediaGases

Identifiers

PMID23296330
PMCPMC3574700
OpenAlexW2089001124

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.