Evidence map›Paper›PMID 39047295›Full record

ReviewACS sensors2024

Levodopa: From Biological Significance to Continuous Monitoring.

David Probst, Kartheek Batchu, John Robert Younce, Koji Sode

Abstract readReview
In one paragraph

Review in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. 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.

David ProbstJoint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, North Carolina 27599, United States.ORCID 0000-0002-8049-6581
Kartheek BatchuJoint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, North Carolina 27599, United States.
John Robert YounceDepartment of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.
Koji SodeJoint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, North Carolina 27599, United States.ORCID 0000-0002-9833-2091

Funding

Prediction of outcomes for deep brain stimulation using multimodal MRIK23NS121630 · NINDS · WASHINGTON UNIVERSITY · PI John Robert Younce · 2022 to 2026
$907k
NINDS NIH HHS K23 NS121630
6 · The paper itself

Abstract

A continuous levodopa sensor can improve the quality of life for patients suffering with Parkinson's disease by enhancing levodopa titration and treatment effectiveness; however, its development is currently hindered by the absence of a specific levodopa molecular recognition element and limited insights into how real-time monitoring might affect clinical outcomes. This gap in research contributes to clinician uncertainty regarding the practical value of continuous levodopa monitoring data. This paper examines the current state of levodopa sensing and the inherent limitations in today's methods. Further, these challenges are described, including aspects such as interference from the metabolic pathway and adjunct medications, temporal resolution, and clinical questions, with a specific focus on a comprehensive selection of molecules, such as adjunct medications and structural isomers, as an interferent panel designed to assess and validate future levodopa sensors. We review insights and lessons from previously reported levodopa sensors and present a comparative analysis of potential molecular recognition elements, discussing their advantages and drawbacks.

Indexed as

LevodopaParkinson DiseaseAntiparkinson AgentsDrug MonitoringHumansAntiparkinson AgentsLevodopaand sensor specificitybiosensor developmentcontinuous levodopa monitoringelectrochemical detectionlevodopa therapymolecular recognition elementsParkinson’s disease

Identifiers

PMID39047295
PMCPMC11348912

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.