Evidence map›Paper›PMID 40422019›Full record

ReviewBiosensors2025

Biosensors for Early Detection of Parkinson's Disease: Principles, Applications, and Future Prospects.

Panpan Jiang, Nan Gao, Gang Chang, Yuxiang Wu

Abstract readReview
In one paragraph

Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. 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

4 authors.

Panpan JiangCollege of Optoelectronic Materials and Technology, Jianghan University, Wuhan 430056, China.
Nan GaoInstitute of Intelligent Sport and Proactive Health, Department of Health and Physical Education, Jianghan University, Wuhan 430056, China.
Gang ChangMinistry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science and Engineering, Hubei University, Wuhan 430062, China.
Yuxiang WuCollege of Optoelectronic Materials and Technology, Jianghan University, Wuhan 430056, China.ORCID 0000-0002-9041-4471

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD), a neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the substantia nigra, imposes substantial economic burdens, including both direct and indirect costs. The medical community currently lacks a definitive cure for Parkinson's disease, and early detection is crucial for timely intervention and disease management. As innovative diagnostic tools, biosensors have shown great potential in detecting PD at its early stages. This review comprehensively summarizes recent advances in biosensors for the early detection of PD, with a particular focus on the detection of two key biomarkers: dopamine (DA) and α-synuclein (α-syn). Furthermore, it illustrates a variety of nanotechnology-based biosensors, including optical, electrochemical, and transistor biosensors, detailing their underlying principles, advantages, limitations, and applications in PD detection. Moreover, the review explores the challenges and prospects of advancing biosensors for early PD diagnosis.

Indexed as

Biosensing TechniquesParkinson Diseasealpha-SynucleinBiomarkersDopamineEarly DiagnosisElectrochemical TechniquesHumansalpha-SynucleinBiomarkersDopaminebiomarkerbiosensorearly diagnosisParkinson’s disease

Identifiers

PMID40422019
PMCPMC12110663

What OpenQuestion holds

Textmetadata
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.