Evidence map›Paper›PMID 41892038›Full record

ArticleBiosensors2026

Sensitive Detection of DJ-1 in Artificial Cerebrospinal Fluid Using a Portable GPTMS-Coordinated Gold Nanoparticle-Based Biosensor.

Münteha Nur Sonuç Karaboğa

Abstract read
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

1 author.

Münteha Nur Sonuç KaraboğaFaculty of Health Sciences, Tekirdag Namik Kemal University, Tekirdag 59030, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A highly selective and sensitive compact immunosensing strategy was developed for the determination of DJ-1, a potential biomarker of Parkinson's disease, one of the leading neurodegenerative disorders, using a portable potentiostat. Initially, screen-printed carbon electrodes (SPCEs) were modified with gold nanoparticles (AuNPs), followed by functionalization with 4-mercapto-1-butanol (MOH). Subsequently, the AuNPs-doped and hydroxyl-functionalized electrodes were treated with 3-glycidoxypropyltrimethoxysilane (GPTMS) to facilitate immobilization of anti-DJ-1 antibodies. Immobilization steps were monitored using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) performed on a bench potentiostat, while the entire analytical performance of the developed biosensor system and its response in artificial cerebrospinal fluid (aCSF) were evaluated by monitoring cathodic current changes with a portable electrochemical reader. The resulting biorecognition element enabled the detection of DJ-1 within the concentration range of 0.001 to 0.3 ng/mL, based on cathodic current changes, achieving a limit of detection as low as 0.00059 ng/mL. Surface morphology and elemental composition alterations were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and energy-dispersive X-ray spectroscopy (EDX). A notable advantage of this GPTMS@AuNPs-based biosensor system is its prolonged storage stability and its capability to accurately quantify DJ-1 in artificial cerebrospinal fluid samples, with recovery rates ranging from 98.66% to 123.3%.

Indexed as

Biosensing TechniquesElectrochemical TechniquesGoldImmunoassayMetal NanoparticlesProtein Deglycase DJ-1SilanesCerebrospinal FluidElectrodesHumansLimit of Detection3-glycidoxypropyltrimethoxysilaneGoldPARK7 protein, humanProtein Deglycase DJ-1SilanesDJ-1glycidoxypropyltrimethoxysilanegold nanoparticlesParkinson’s diseaseportable biosensor

Identifiers

PMID41892038
PMCPMC13023608

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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.