Evidence map›Paper›PMID 39852053›Full record

ReviewBiosensors2024

Recent Advances in Biosensors Using Enzyme-Stabilized Gold Nanoclusters.

Myeong-Jun Lee, Jeong-Hyeop Shin, Seung-Hun Jung, Byung-Keun Oh

Abstract readReview
In one paragraph

Review in Biosensors, 2024. 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. Review
  2. Review
  3. 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

4 authors.

Myeong-Jun LeeDepartment of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Republic of Korea.
Jeong-Hyeop ShinDepartment of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Republic of Korea.
Seung-Hun JungDepartment of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Republic of Korea.
Byung-Keun OhDepartment of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, Republic of Korea.ORCID 0000-0002-3268-4705

Funding

Korea Institute of Energy Technology Evaluation and Planning 20214000000500National Research Foundation of Korea NRF-2022R1A2C1006714
6 · The paper itself

Abstract

Recently, gold nanoclusters (AuNCs) have been widely used in biological applications due to their ultrasmall size, ranging within a few nanometers; large specific surface area; easy functionalization; unique fluorescence properties; and excellent conductivity. However, because they are unstable in solution, AuNCs require stabilization by using ligands such as dendrimers, peptides, DNA, and proteins. As a result, the properties of AuNCs and their formation are determined by the ligand, so the selection of the ligand is important. Of the many ligands implemented, enzyme-stabilized gold nanoclusters (enzyme-AuNCs) have attracted increasing attention for biosensor applications because of the excellent optical/electrochemical properties of AuNCs and the highly target-specific reactions of enzymes. In this review, we explore how enzyme-AuNCs are prepared, their properties, and the various types of enzyme-AuNC-based biosensors that use optical and electrochemical detection techniques. Finally, we discuss the current challenges and prospects of enzyme-AuNCs in biosensing applications. We expect this review to provide interdisciplinary knowledge about the application of enzyme-AuNC-based materials within the biomedical and environmental fields.

Indexed as

Biosensing TechniquesGoldMetal NanoparticlesElectrochemical TechniquesEnzymesEnzymesGoldbiosensorelectrochemicalenzymegold nanoclusteroptical

Identifiers

PMID39852053
PMCPMC11763740

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.