Translating Adenosine A24 Receptor Biology into Novel Therapies for Parkinson's Disease
Bibliographic record
Abstract
Recent advances in the pharmacology, neurotoxicology and epidemiology of the adenosine A2A receptor have provided evidence that A2A receptor antagonists (including caffeine) may offer therapeutic benefits in Parkinson's disease (PD) at multiple levels. Not only does A2A receptor blockade reduce the symptomatic psychomotor slowing characteristic of PD, but based on recent preclinical data on rodents and non-human primates A2a receptor blockade potentially can attenuate neurotoxin-induced dopaminergic neuron loss and the development of maladaptive (dyskinetic) responses to chronic dopaminergic therapy. The conference and post-conference publication have been organized to systematically explore the role of the A2A receptor in PD through sequential themes leading from A2AR, neurobiology to the development of clinical trials for A2A antagonists in PD. The purpose of our post-conference publication a special supplement issue of the journal Neurology is to broad disseminate the information generated by the conference to a wide audience of basic and clinical neuroscientists in academics, government and industry. Given this journal's high profile and direct distribution of 20,000 as well as PubMed indexing, the publication will markedly enhance the dissemination of information coming out of the conference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".