ArticleMikrochimica acta2025
Urea-mediated growth engineering of Au@Ag core-shell nanostructures: an enzymatic detection strategy with machine learning-assisted comparative analysis.
Article in Mikrochimica acta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A non-invasive, enzyme-based colorimetric biosensor was developed for urea detection in saliva, utilizing a growth-based method with Au@Ag core-shell nanostructures, including CTAB-coated gold nanoparticles (AuNPs) and CTAB-coated gold nanorods with short (SAuNRs) and high (HAuNRs) aspect ratios. The biosensing mechanism relies on urease-mediated hydrolysis of urea, which raises the pH and enhances the reduction capability of ascorbic acid, leading to the formation of a silver shell on the gold nanostructures, causing colorimetric changes correlated to urea concentration. A machine-learning comparative analysis was also performed to assess how the nanostructure morphology of AuNPs, SAuNRs, and HAuNRs affects sensor performance. The results showed that the biosensor effectively detected urea with all three nanostructures, achieving the highest precision and accuracy using HAuNRs, with a limit of detection (LOD) of 0.26 mM and a linear range of 0.5-50 mM urea. The best-performing machine learning algorithms, evaluated using R
Indexed as
Identifiers
40730702What OpenQuestion holds
Registered trials
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.