Evidence map›Paper›PMID 33374711›Full record

ArticleBiosensors2020

A Label-Free Optical Detection of Pathogens in Isopropanol as a First Step towards Real-Time Infection Prevention.

Julie Claudinon, Siegfried Steltenkamp, Manuel Fink, Taras Sych, Benoît Verreman, Winfried Römer, Morgan Madec

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
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 3 institutions in 2 countries.

Julie ClaudinonFaculty of Biology, University of Freiburg, 79104 Freiburg, Germany.
Siegfried SteltenkampSignalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany.
Manuel FinkFaculty of Biology, University of Freiburg, 79104 Freiburg, Germany.
Taras SychFaculty of Biology, University of Freiburg, 79104 Freiburg, Germany.
Benoît VerremanOphardt Hygiene-Technik GmbH + Co. KG, 47661 Issum, Germany.
Winfried RömerFaculty of Biology, University of Freiburg, 79104 Freiburg, Germany.ORCID 0000-0002-2847-246X
Morgan MadecSignalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany.ORCID 0000-0001-9537-642X
University of Freiburg · DECentre National de la Recherche Scientifique · FRUniversité de Strasbourg · FR

Funding

Bundesministerium für Bildung und Forschung 16ES0748Deutsche Forschungsgemeinschaft EXC 294
6 · The paper itself

Abstract

The detection of pathogens is a major public health issue. Every year, thousands of people die because of nosocomial infections. It is therefore important to be able to detect possible outbreaks as early as possible, especially in the hospital environment. Various pathogen detection techniques have already been demonstrated. However, most of them require expensive and specific equipment, and/or complex protocols, which, most of the time, involve biochemical reaction and labelling steps. In this paper, a new method that combines microscopic imaging and machine learning is described. The main benefits of this approach are to be low-cost, label-free and easy to integrate in any suitable medical device, such as hand hygiene dispensers. The suitability of this pathogen detection method is validated using four bacteria, both in PBS (Phosphate Buffered Saline) and in isopropanol. In particular, we demonstrated an efficient pathogenic detection that is sensible to changes in the composition of a mixture of pathogens, even in alcohol-based solutions.

Indexed as

2-PropanolMicroscopyBacteriaHumansMachine Learning2-Propanolclassificationimage processingmachine learningmicroscopyoptical detectionpathogens

Identifiers

PMID33374711
PMCPMC7822415
OpenAlexW3116352301

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.