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Record W2977407036 · doi:10.1371/journal.pone.0223450

Botulinum toxin A injection into the entopeduncular nucleus improves dynamic locomotory parameters in hemiparkinsonian rats

2019· article· en· W2977407036 on OpenAlexaff
Adrianna R Tsang, Nagalingam Rajakumar, Mandar Jog

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsLondon Health Sciences CentreWestern University
FundersMerz Pharmaceuticals
KeywordsSubthalamic nucleusDeep brain stimulationBotulinum toxinGaitBasal gangliaGait DisturbanceParkinson's diseaseNeuroscienceGlobus pallidusMedicineGait analysisPhysical medicine and rehabilitationPsychologyCentral nervous systemInternal medicineDisease

Abstract

fetched live from OpenAlex

Parkinson's disease is associated with hyperactivity of the subthalamic nucleus (STN), contributing to motor and gait disturbances. Although deep brain stimulation of the STN alleviates certain motor dysfunction, its specific effect on gait abnormalities remains controversial. This study investigated the long-term changes in locomotion following direct infusions of botulinum toxin-A into the globus pallidus internal segment (GPi) to suppress the flow of information from the STN to the GPi in a hemiparkinsonian rat model. Static and dynamic gait parameters were quantified using a CatWalk apparatus. Interestingly, botulinum toxin-A at 0.5 ng significantly reduced only the dynamic gait parameters of hemiparkinsonian rats at 1 week and 1 month post-infusion, while static gait parameters did not change. This study offers new insights into the complexity of basal ganglia in locomotor control and shows the potential of central infusion of botulinum toxin-A as a novel intervention in the study of experimental hemiparkinson's disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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