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Cabergoline scavenges peroxynitrite enhanced by <scp>l</scp>‐DOPA therapy in patients with Parkinson's disease

2006· article· en· W2001994239 on OpenAlexfundno aff
Chiaki Isobe, Takashi Abe, Toshihiro Kikuchi, T Murata, Chigumi Sato, Yasuo Terayama

Bibliographic record

VenueEuropean Journal of Neurology · 2006
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsCabergolineMedicineDopaminergicPeroxynitriteParkinson's diseaseNitrotyrosineOxidative stressDopamineCerebrospinal fluidInternal medicineDiseasePharmacologyGastroenterologyBiochemistryChemistryNitric oxide

Abstract

fetched live from OpenAlex

Long-term or high-dose L-DOPA therapy in patients with Parkinson's disease (PD) may accelerate degeneration of dopaminergic neurons, possibly by increasing oxidative stress. To investigate the effects of cabergoline on peroxynitrite-mediated oxidative damage caused by L-DOPA, the concentration of 3-nitrotyrosine in cerebrospinal fluid (CSF) of 18 PD patients was compared with that in 20 normal controls. The concentration of 3-nitrotyrosine in patients following L-DOPA therapy was significantly higher than in untreated PD patients and controls. On the other hand, the concentration in PD patients after cabergoline therapy was significantly lower than in PD patients after L-DOPA therapy alone. These data suggest that cabergoline scavenges peroxynitrite induced by L-DOPA in patients with PD.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 designObservational
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

Citations9
Published2006
Admission routes1
Has abstractyes

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Same venueEuropean Journal of NeurologySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207