Evidence map›Paper›PMID 32204503›Full record

ReviewSensors (Basel, Switzerland)2020

Electrochemical Biosensors Based on S-Layer Proteins.

Samar Damiati, Bernhard Schuster

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 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

2 authors.

Samar DamiatiDepartment of Biochemistry, Faculty of Science, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia.ORCID 0000-0002-6747-0408
Bernhard SchusterInstitute for Synthetic Bioarchitectures, Department of NanoBiotechnology, BOKU - University of Natural Resources and Life Sciences, Vienna, Muthgasse 11, 1190 Vienna, Austria.

Funding

Austrian Science Fund P 29399-B22Austrian Science Fund FWF P 29399
6 · The paper itself

Abstract

Designing and development of electrochemical biosensors enable molecule sensing and quantification of biochemical compositions with multitudinous benefits such as monitoring, detection, and feedback for medical and biotechnological applications. Integrating bioinspired materials and electrochemical techniques promote specific, rapid, sensitive, and inexpensive biosensing platforms for (e.g., point-of-care testing). The selection of biomaterials to decorate a biosensor surface is a critical issue as it strongly affects selectivity and sensitivity. In this context, smart biomaterials with the intrinsic self-assemble capability like bacterial surface (S-) layer proteins are of paramount importance. Indeed, by forming a crystalline two-dimensional protein lattice on many sensors surfaces and interfaces, the S-layer lattice constitutes an immobilization matrix for small biomolecules and lipid membranes and a patterning structure with unsurpassed spatial distribution for sensing elements and bioreceptors. This review aims to highlight on exploiting S-layer proteins in biosensor technology for various applications ranging from detection of metal ions over small organic compounds to cells. Furthermore, enzymes immobilized on the S-layer proteins allow specific detection of several vital biomolecules. The special features of the S-layer protein lattice as part of the sensor architecture enhances surface functionalization and thus may feature an innovative class of electrochemical biosensors.

Indexed as

Biosensing TechniquesElectrochemical TechniquesHumansIonsMembrane GlycoproteinsMetalsPoint-of-Care TestingIonsMembrane GlycoproteinsMetalsS-layer proteinsbiocompatible layerbioinspired materialbiosensorself-assemblyS-layer proteins

Identifiers

PMID32204503
PMCPMC7147708

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.