Evidence map›Paper›PMID 35200363›Full record

ArticleBiosensors2022

Differential Amperometric Microneedle Biosensor for Wearable Levodopa Monitoring of Parkinson's Disease.

Lu Fang, Hangxu Ren, Xiyu Mao, Shanshan Zhang, Yu Cai, Shiyi Xu, Yi Zhang, Lihua Li, Xuesong Ye, Bo Liang

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
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

11 citing papers in PubMed, 30 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Lu FangDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Hangxu RenBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Xiyu MaoBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Shanshan ZhangBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Yu CaiBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Shiyi XuBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Yi ZhangDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Lihua LiDepartment of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Xuesong YeBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.
Bo LiangBiosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-3670-2296
Zhejiang University · CNHangzhou Dianzi University · CN

Funding

National Natural Science Foundation of China 61501400, 81501555Zhejiang Provincial Natural Science Foundation LQ20F010011, LY18H180006
6 · The paper itself

Abstract

Levodopa (L-Dopa) is considered to be one of the most effective therapies available for Parkinson's disease (PD) treatment. The therapeutic window of L-Dopa is narrow due to its short half-life, and long-time L-Dopa treatment will cause some side effects such as dyskinesias, psychosis, and orthostatic hypotension. Therefore, it is of great significance to monitor the dynamic concentration of L-Dopa for PD patients with wearable biosensors to reduce the risk of complications. However, the high concentration of interferents in the body brings great challenges to the in vivo monitoring of L-Dopa. To address this issue, we proposed a minimal-invasive L-Dopa biosensor based on a flexible differential microneedle array (FDMA). One working electrode responded to L-Dopa and interfering substances, while the other working electrode only responded to electroactive interferences. The differential current response of these two electrodes was related to the concentration of L-Dopa by eliminating the common mode interference. The differential structure provided the sensor with excellent anti-interference performance and improved the sensor's accuracy. This novel flexible microneedle sensor exhibited favorable analytical performance of a wide linear dynamic range (0-20 μM), high sensitivity (12.618 nA μM

Indexed as

Biosensing TechniquesParkinson DiseaseWearable Electronic DevicesElectrodesHumansLevodopaLevodopabiosensorflexible differential microneedle array (FDMA)levodopa (L-Dopa)minimal-invasiveParkinson’s disease (PD)

Identifiers

PMID35200363
PMCPMC8869619
OpenAlexW4210928304

What OpenQuestion holds

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LicenceCC BY
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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.