Evidence map›Paper›PMID 40883378›Full record

ArticleScientific reports2025

Accelerometry is a valid method to distinguish between healthy and 6-OHDA-lesioned parkinsonian rats.

Johannes Otto, Meike Statz, Hanna Weber, Maximilian Koschay, Maria Kober, Franz Plocksties, Dirk Timmermann, Christian Haubelt, Alexander Storch, Mareike Fauser and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Johannes Otto *Institute of Communications Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany. johannes.otto@uni-rostock.de.
Meike Statz *Department of Neurology, University Medical Center Rostock, University of Rostock, Gehlsheimer Str. 20, Rostock, 18147, Germany.
Hanna WeberDepartment of Neurology, University Medical Center Rostock, University of Rostock, Gehlsheimer Str. 20, Rostock, 18147, Germany.
Maximilian KoschayInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.
Maria KoberDepartment of Neurology, University Medical Center Rostock, University of Rostock, Gehlsheimer Str. 20, Rostock, 18147, Germany.
Franz PlockstiesInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.
Dirk TimmermannInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.
Christian HaubeltInstitute of Applied Microelectronics and Computer Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.
Alexander StorchDepartment of Neurology, University Medical Center Rostock, University of Rostock, Gehlsheimer Str. 20, Rostock, 18147, Germany.
Mareike FauserDepartment of Neurology, University Medical Center Rostock, University of Rostock, Gehlsheimer Str. 20, Rostock, 18147, Germany.
Florian Grützmacher *Institute of Applied Microelectronics and Computer Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.
Sascha Spors *Institute of Communications Engineering, University of Rostock, Albert-Einstein-Str. 26, Rostock, 18059, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In Parkinson's disease (PD), continuous sensor-based evaluation of motor symptom severity, e.g., using accelerometry, has become an emerging field of interest in clinical research. Continuous symptom monitoring would also be of interest in preclinical disease models; however, such devices are far less established in animal models, most likely due to additional requirements in size, energy consumption, and impairment-free attachment. In contrast, accelerometers manufactured in micro-electro-mechanical systems (MEMS) technology are promising sensor devices, which allow for space-saving and energy-efficient monitoring of movements. In the present study, we aim to extend the state of the art by establishing wireless accelerometer measurements as a simple and energy-efficient method to distinguish between healthy rats and the 6-hydroxydopamine (6-OHDA) PD animal model. Male Wistar-Han rats were assessed either three weeks after unilateral 6-OHDA or sham lesioning within their home cages with an extracorporeal accelerometer placed in a rodent backpack for 12 h during their active phase. The data was transmitted wirelessly to a computer, preprocessed, and a statistical analysis was performed to find differences between the datasets of 6-OHDA and sham-lesioned rats. The statistical analysis showed significant differences in the variances of the magnitude of the acceleration vectors between the two classes. In conclusion, accelerometry is a valid method to distinguish between 6-OHDA-lesioned rats with unilateral dopaminergic deficiency and their healthy counterparts. The presented method represents a first step towards automated symptom severity monitoring and provides a framework to expand the application to on-implant integrated accelerometers for continuous monitoring of symptom manifestations in rodent models of neurodegenerative diseases. Future studies are required to expand accelerometry to assess symptom severity to ultimately utilize it for preclinical research on adaptive therapies.

Indexed as

AccelerometryOxidopamineParkinson DiseaseAnimalsDisease Models, AnimalMaleRatsRats, WistarWireless TechnologyOxidopamine6-hydroxydopamineAccelerometryBiomarkersMEMS accelerometerParkinson’s disease

Identifiers

PMID40883378
PMCPMC12397390

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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.