The Anti-Parkinsonian A2A Receptor Antagonist Istradefylline (KW-6002) Attenuates Behavioral Abnormalities, Neuroinflammation, and Neurodegeneration in Cerebral Ischemia: Adenosinergic Signaling Link Between Stroke and Parkinson’s Disease
Bibliographic record
Abstract
Stroke, the third leading cause of death worldwide, is a major cause of functional disability. Cerebral ischemia causes rapid elevation of adenosine, the main neuromodulator in the brain. Inhibition of adenosine A2A receptors (A2ARs) has been introduced as a potential target in neurodegenerative disorders involving extracellular adenosine elevation. Istradefylline, a selective A2AR antagonist, has been approved for Parkinson’s disease (PD) adjunctive therapy and showed neuroprotective effects in PD and Alzheimer’s disease. However, the role of A2ARs in post-stroke neuronal damage and behavioral deficits remains unclear. We recently showed that A2AR antagonism prevented the adenosine-induced post-hypoxia synaptic potentiation of glutamatergic neurotransmission following hypoxia/reperfusion of hippocampal slices. Here, we investigated the potential neuroprotective effects of istradefylline in male Sprague-Dawley rats subjected to pial vessel disruption (PVD) used to model a small vessel stroke. Rats were treated with either vehicle control or istradefylline (3 mg/kg i.p.) following PVD surgery for three days. Istradefylline administration prevented anxiety and depressive-like behaviors caused by PVD stroke. In addition, istradefylline significantly attenuated ischemia-induced cognitive impairment and motor deficits. Moreover, istradefylline markedly reduced hippocampal neurodegeneration, GFAP/Iba-1, TNF-α, nNOS and iNOS levels after PVD, but prevented the downregulation of anti-inflammatory markers TGF-β1 and IL-4. Together, the results suggest a molecular link between stroke and PD and that the anti-PD drug istradefylline displays translational potential for drug repurposing as a neuroprotective agent for cerebral ischemic damage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".