Evidence map›Paper›PMID 35025087›Full record

ArticleBrazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]2022

Fast antibiotic susceptibility testing of urine microflora using a microbiological analyzer based on coherent fluctuation nephelometry.

Alexander S Gur'ev, Margus Tigasson, Olga Yu Shalatova, Stanislav F Rastopov, Anastasia Bilozor, Marina Ivanova, Alexey Yu Volkov

Abstract read
In one paragraph

Article in Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology], 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Deep Learning-Based Rapid Identification ofRisk management and healthcare policy · 2026
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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.

Alexander S Gur'evScientific-Research Laboratory, M.F. Vladimirsky Moscow Regional Clinical and Research Institute (MONIKI), Shchepkina str. 61/2, b.1, 129110, Moscow, Russian Federation. coherneph@mail.ru.ORCID http://orcid.org/0000-0001-8823-7819
Margus TigassonDepartment of Microbiology, Central Laboratory, East-Tallinn Central Hospital, Ravi 18, 10138, Tallinn, Estonia.
Olga Yu ShalatovaLaboratory of Biopreparations, Innovative Technologies Department, Pasteur Institute of Epidemiology and Microbiology, Mira str. 14, 197101, St. Petersburg, Russian Federation.ORCID http://orcid.org/0000-0001-9232-7737
Stanislav F RastopovOptical Spectroscopy Department, A.M. Prokhorov General Physics Institute of the Russian Academy of Sciences, Vavilov str. 38, 119991, Moscow, Russian Federation.ORCID http://orcid.org/0000-0002-9732-6271
Anastasia BilozorDepartment of Microbiology, Central Laboratory, East-Tallinn Central Hospital, Ravi 18, 10138, Tallinn, Estonia.
Marina IvanovaDepartment of Microbiology, Central Laboratory, East-Tallinn Central Hospital, Ravi 18, 10138, Tallinn, Estonia.
Alexey Yu VolkovMedtechnopark Ltd, Profsoyuznaya str. 8-2-383, 117292, Moscow, Russian Federation.ORCID http://orcid.org/0000-0001-5110-553X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditional culture-based microbiological methods remain the most used for defining the etiology of urinary tract infections and antibiotic susceptibility testing (AST) of isolated uropathogens. They are time-consuming and lead to delays of several days when obtaining the final results of microbiological tests.

objectivesIn this study, we validate the possibility of using a microbiological CFN analyzer combined with MALDI-TOF mass spectrometry (MS) for fast conclusive urine testing (1 day) without obtaining pure cultures. MATERIALS AND

methodsThe study included three stages: detection of urine microflora growth using the CFN analyzer to separate positive and negative samples within 2-4 h; fast MS identification of positive samples without isolating uropathogens; fast AST using CFN analyzer within 3-6 h. In parallel, all urine samples were tested by traditional culture-based microbiological methods.

resultIn total, 194 urine samples were tested, and 22 urine cultures were identified by MS, among them, 20 monocultures with bacterial counts ≥ 10

conclusionsThe CFN analyzer is promising and effective for fast AST. Combined with MS identification, it allows to perform full urine analysis in 1 day without the lengthy isolation of pure cultures.

Indexed as

Urinary Tract InfectionsAnti-Bacterial AgentsHumansMicrobial Sensitivity TestsMicrobiological TechniquesNephelometry and TurbidimetrySpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAnti-Bacterial AgentsAntibiotic susceptibility testingBacteriuriaCoherent fluctuation nephelometryMALDI-TOF mass spectrometryUrinary tract infection

Identifiers

PMID35025087
PMCPMC8882479

What OpenQuestion holds

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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.