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Record W4411241691 · doi:10.18192/osurj.v4i1.7376

Therapeutic Approaches to Restore Dopamine Homeostasis and Alleviate Motor Symptoms in Parkinson’s Disease – a Narrative Review

2025· review· en· W4411241691 on OpenAlexaffvenue
Jordan J. Yin, Josh A. Zeldin, Huu Phuc Nguyen

Bibliographic record

VenueUniversity of Ottawa Science Undergraduate Research Journal · 2025
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsParkinson's diseaseDopamineNarrative reviewNarrativeNeuroscienceMedicineDiseaseHomeostasisMotor symptomsPsychologyIntensive care medicineInternal medicineArt

Abstract

fetched live from OpenAlex

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized by progressive dopaminergic neuronal loss in the substantia nigra pars compacta, leading to motor impairments such as bradykinesia, tremors, and rigidity. Current therapeutic strategies aim to restore dopamine homeostasis and alleviate motor symptoms through pharmacological, surgical, and non-pharmacological interventions; however, there is no current treatments that treat PD as a whole. This narrative review highlights three primary pharmacological approaches: (1) dopamine replacement with levodopa, the gold standard therapy, which is effective but associated with long-term complications such as motor fluctuations and dyskinesias; (2) dopamine receptor agonists, which offer an alternative to levodopa but exhibit increased non-motor side effects; and (3) inhibitors of dopamine degradation enzymes, including monoamine oxidase B (MAO-B) and catechol-O-methyltransferase (COMT) inhibitors, which prolong dopamine availability and reduce motor fluctuations. Emerging strategies focus on multifunctional compounds targeting both MAO-B and COMT, offering neuroprotection and improved dopaminergic stability. Despite advancements in PD management, an urgent need remains for novel therapeutics that provide sustained symptom relief with minimal side effects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.154
GPT teacher head0.373
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes2
Has abstractyes

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