Evidence map›Paper›PMID 37420966›Full record

ArticleMicromachines2023

Microbial Biosensor for Characterization of a Microorganism: A Review focusing on the Biochemical Activity of Microbial Cells.

Elena Emelyanova

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

1 author at 1 institution in 1 country.

Elena EmelyanovaLaboratory of Biosensors, G.K. Skryabin Institute of Biochemistry and Physiology of Microorganisms, Pushchino Center for Biological Research of the Russian Academy of Sciences, Prosp. Nauki 5, Pushchino 142290, Russia.ORCID 0000-0002-2651-681X
G. K. Skryabin Institute of Biochemistry and Physiology of Microorganisms · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Express assessment of the biochemical activity of microorganisms is important in both applied and fundamental research. A laboratory model of a microbial electrochemical sensor formed on the basis of the culture of interest is a device that provides rapidly information about the culture and is cost effective, simple to fabricate and easy to use. This paper describes the application of laboratory models of microbial sensors in which the Clark-type oxygen electrode was used as a transducer. The formation of the models of the reactor microbial sensor (RMS) and the membrane microbial sensor (MMS) and the formation of the response of biosensors are compared. RMS and MMS are based on intact or immobilized microbial cells, respectively. For MMS, the response of biosensor is caused both by the process of transport of substrate into microbial cells and by the process of the initial metabolism of substrate; and only initial substrate metabolism triggers the RMS response. The details of the application of biosensors for the study of allosteric enzymes and inhibition by substrate are discussed. For inducible enzymes, special attention is paid to the induction of microbial cells. This article addresses current problems related to implementation of the biosensor approach and discusses the ways how to overcome these problems.

Indexed as

allosteric enzymesClark-type oxygen electrodeconstitutive and inducible enzymesmembrane microbial sensoroxygen consumption by microbial cellsreactor microbial sensorsubstrate transport into microbial cells

Identifiers

PMID37420966
PMCPMC10141839
OpenAlexW4361007336

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.