Evidence map›Paper›PMID 41254219›Full record

ArticleScientific reports2025

Bovine serum albumin-protected copper nanoclusters as a label-free biosensor for the discrimination of bacterial strains.

Saad Megahed, Noha El Salakawy, Wael Mamdouh

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Saad MegahedDepartment of Chemistry, School of Sciences and Engineering, The American University in Cairo (AUC), New Cairo, 11835, Egypt.
Noha El SalakawyDepartment of Chemistry, School of Sciences and Engineering, The American University in Cairo (AUC), New Cairo, 11835, Egypt.
Wael MamdouhDepartment of Chemistry, School of Sciences and Engineering, The American University in Cairo (AUC), New Cairo, 11835, Egypt. wael_mamdouh@aucegypt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of biosensors for infectious diseases is crucial, especially in the post pandemic era. Point-of-care (POC) devices are highly needed in developing countries to provide quick and cost-effective results. Nanoparticles with enzymatic-like activity, also known as nanozymes, show great promise for diagnostic applications and have recently gained significant attention. In this study, bovine serum albumin (BSA)-protected copper nanoclusters (Cu NCs) with enzymatic-like characteristics were created and analysed for their intrinsic enzymatic-like activity, physicochemical properties, and colloidal stability. The Cu NCs were synthesized via the use of BSA as a capping and reducing agent to ensure their biocompatibility and colloidal stability. Various techniques, such as UV‒Vis spectroscopy, photoluminescence (PL), dynamic light scattering (DLS), elemental analysis, and transmission electron microscopy (TEM), have been used to characterize the prepared Cu NCs. The Cu NCs demonstrated oxidase- and peroxidase-like activity by catalyzing the oxidation of o-phenylenediamine (OPD) into 2,3-diaminophenazine (DAP), the oxOPD product. Showing higher oxidase-like activity by 1.6x compared to the peroxidase-like activity at the same substrate concentration. Their catalytic properties were utilized to detect two common bacteria, gram-negative Escherichia coli (E. coli) and gram-positive Staphylococcus aureus (S. aureus), demonstrating a differential label-free detection of different strains at concentrations higher than 10

Indexed as

Biosensing TechniquesCopperEscherichia coliMetal NanoparticlesSerum Albumin, BovineStaphylococcus aureusAnimalsCattleCopperSerum Albumin, BovineBacterial detectionCopper nanoclustersPeroxidase-like activitySelective label-free biosensor

Identifiers

PMID41254219
PMCPMC12627415

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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.