ReviewJournal of clinical medicine2024
Biochemical Sensors for Personalized Therapy in Parkinson's Disease: Where We Stand.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled 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.
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
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Computer Vision Technologies in Movement Disorders: A Systematic Review.Movement disorders clinical practice · 2025Pooled it
- Computer Vision Analysis for Objective Motor Assessment in Parkinson's Disease: A Retrospective Study.Movement disorders clinical practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its first introduction, levodopa has remained the cornerstone treatment for Parkinson's disease. However, as the disease advances, the therapeutic window for levodopa narrows, leading to motor complications like fluctuations and dyskinesias. Clinicians face challenges in optimizing daily therapeutic regimens, particularly in advanced stages, due to the lack of quantitative biomarkers for continuous motor monitoring. Biochemical sensing of levodopa offers a promising approach for real-time therapeutic feedback, potentially sustaining an optimal motor state throughout the day. These sensors vary in invasiveness, encompassing techniques like microdialysis, electrochemical non-enzymatic sensing, and enzymatic approaches. Electrochemical sensing, including wearable solutions that utilize reverse iontophoresis and microneedles, is notable for its potential in non-invasive or minimally invasive monitoring. Point-of-care devices and standard electrochemical cells demonstrate superior performance compared to wearable solutions; however, this comes at the cost of wearability. As a result, they are better suited for clinical use. The integration of nanomaterials such as carbon nanotubes, metal-organic frameworks, and graphene has significantly enhanced sensor sensitivity, selectivity, and detection performance. This framework paves the way for accurate, continuous monitoring of levodopa and its metabolites in biofluids such as sweat and interstitial fluid, aiding real-time motor performance assessment in Parkinson's disease. This review highlights recent advancements in biochemical sensing for levodopa and catecholamine monitoring, exploring emerging technologies and their potential role in developing closed-loop therapy for Parkinson's disease.
Indexed as
Identifiers
What 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.