Evidence map›Paper›PMID 42076186›Full record

ArticleMicromachines2026

Ultrasensitive Human Urinary Albumin Detection via Composite Nanohydrogels.

Özge Altıntaş, Fatma Yılmaz, Elif Serra Taş, Adil Denizli

Abstract read
In one paragraph

Article in Micromachines, 2026. 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
–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

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

4 authors.

Özge AltıntaşDepartment of Chemistry, Hacettepe University, 06800 Ankara, Turkey.ORCID 0000-0001-8015-3004
Fatma YılmazDepartment of Chemistry and Chemical Processing Technologies, Bolu Abant İzzet Baysal University, 14900 Bolu, Turkey.ORCID 0000-0003-3260-1639
Elif Serra TaşDepartment of Molecular Biology and Genetics, Başkent University, 06790 Ankara, Turkey.ORCID 0009-0001-6271-9673
Adil DenizliDepartment of Chemistry, Hacettepe University, 06800 Ankara, Turkey.ORCID 0000-0001-7548-5741

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Albumin is an important biomarker in biological fluids and plays a critical role, particularly in the diagnosis of renal dysfunction. Therefore, the sensitive detection of low concentrations of albumin in urine is of great importance. In this study, a composite nanohydrogel modified with carbon dots has been developed for the selective detection of albumin from human urine. The composite nanohydrogels were synthesised using a molecular imprinting technique specifically designed to recognise albumin. Characterisation studies were conducted using ZetaSizer, SEM, EDX, CLSM and ATR-FTIR methods. The albumin-binding capacities of the carbon dots (C-Dots) and synthesised composite nanohydrogels were evaluated using fluorescence spectroscopy. The effects of different concentration conditions on binding efficiency were systematically investigated. Selectivity studies have shown that albumin-imprinted nanohydrogels can detect target molecule albumin four times more selectively than competitive molecules, Hb and IgG. Imprinting efficiency was estimated by comparing the signals of albumin obtained from non-imprinted and albumin-imprinted composite nanohydrogels. Finally, artificial urine samples mimicking real biological environment conditions were examined to evaluate matrix effect on the albumin detection. The repeatability and long-term stability of albumin detection, performed with four consecutive and six-month measurements, was evaluated using the %RSD value, confirming that the albumin determination performance was maintained.

Indexed as

albuminbiomolecule detectionC-Dotsmolecular imprinting technologynanohydrogel

Identifiers

PMID42076186
PMCPMC13117906

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Registered trials

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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.