Evidence map›Paper›PMID 32408710›Full record

ReviewBiomimetics (Basel, Switzerland)2020

Advances in Biomimetic Systems for Molecular Recognition and Biosensing.

Yeşeren Saylan, Özgecan Erdem, Fatih Inci, Adil Denizli

Abstract readReview
In one paragraph

Review in Biomimetics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 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

4 authors.

Yeşeren SaylanDepartment of Chemistry, Hacettepe University, 06800 Ankara, Turkey.
Özgecan ErdemDepartment of Biology, Hacettepe University, 06800 Ankara, Turkey.
Fatih InciUNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
Adil DenizliDepartment of Chemistry, Hacettepe University, 06800 Ankara, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the fundamentals of natural design, structure, and function has pushed the limits of current knowledge and has enabled us to transfer knowledge from the bench to the market as a product. In particular, biomimicry-one of the crucial strategies in this respect-has allowed researchers to tackle major challenges in the disciplines of engineering, biology, physics, materials science, and medicine. It has an enormous impact on these fields with pivotal applications, which are not limited to the applications of biocompatible tooth implants, programmable drug delivery systems, biocompatible tissue scaffolds, organ-on-a-chip systems, wearable platforms, molecularly imprinted polymers (MIPs), and smart biosensors. Among them, MIPs provide a versatile strategy to imitate the procedure of molecular recognition precisely, creating structural fingerprint replicas of molecules for biorecognition studies. Owing to their affordability, easy-to-fabricate/use features, stability, specificity, and multiplexing capabilities, host-guest recognition systems have largely benefitted from the MIP strategy. This review article is structured with four major points: (i) determining the requirement of biomimetic systems and denoting multiple examples in this manner; (ii) introducing the molecular imprinting method and reviewing recent literature to elaborate the power and impact of MIPs on a variety of scientific and industrial fields; (iii) exemplifying the MIP-integrated systems, i.e., chromatographic systems, lab-on-a-chip systems, and sensor systems; and (iv) closing remarks.

Indexed as

biomimeticbiorecognitionbiosensingmolecularly imprinted systems

Identifiers

PMID32408710
PMCPMC7345028

What OpenQuestion holds

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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.