Adenosine A2A signaling in mood disorders: How far have we come?
Bibliographic record
Abstract
Over the years, evidence has continued to support the role of the adenosinergic system in shaping emotional behavior and its impact on the development of mood disorders. In the 1980s, pioneering studies revealed that tricyclic antidepressants could modulate the levels of adenosine and adenosine metabolism. Moreover, evidence from animal models support a regulation of adenosine receptors in brain regions involved in emotional responses, and the role of pharmacological manipulation of adenosine receptors on behavioral despair, anxiety, locomotion, rewards processing and cognition. Clinical research has focused on the effects of caffeine, a non-selective adenosine receptor antagonist, and in genetic polymorphisms in adenosine receptors on emotional regulation in psychiatric patients. Recently, the approval of Istradefylline as an adjunctive treatment for Parkinson's disease holds great promise to expand our understanding of adenosine A2A receptors (A2AR) blockade in humans. Furthermore, recent advancements in transgenic lines and optogenetic techniques have highlighted the role of adenosine receptors in specific cell types and brain circuits that control emotional behavior. In this review, our focus will be on A2AR, given their strong association to stress-related conditions, mood disorders, and the potential of A2AR antagonists in clinical research. We will discuss the progress achieved in understanding its role in emotional regulation, emphasizing functions across distinct cell types and potential applications as a pharmacological target for mood disorders in the upcoming years.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".