A1 Adenosine Receptor Upregulation and Activation Attenuates Neuroinflammation and Demyelination in a Model of Multiple Sclerosis
Bibliographic record
Abstract
The neuromodulator adenosine regulates immune activation and neuronal survival through specific G-protein-coupled receptors expressed on macrophages and neurons, including the A1 adenosine receptor (A1AR). Here we show that A1AR null (A1AR-/-) mice developed a severe progressive-relapsing form of experimental allergic encephalomyelitis (EAE) compared with their wild-type (A1AR+/+) littermates. Worsened demyelination, axonal injury, and enhanced activation of microglia/macrophages were observed in A1AR-/- animals. In addition, spinal cords from A1AR-/- mice demonstrated increased proinflammatory gene expression during EAE, whereas anti-inflammatory genes were suppressed compared with A1AR+/+ animals. Macrophages from A1AR-/- animals exhibited increased expression of the proinflammatory genes, interleukin-1beta, and matrix metalloproteinase-12 on immune activation when matched with A1AR+/+ control cells. A1AR-/- macrophage-derived soluble factors caused significant oligodendrocyte cytotoxicity compared with wild-type controls. The A1AR was downregulated in microglia in A1AR+/+ mice during EAE accompanied by neuroinflammation, which recapitulated findings in multiple sclerosis (MS) patients. Caffeine treatment augmented A1AR expression on microglia, with ensuing reduction of EAE severity, which was further enhanced by concomitant treatment with the A1AR agonist, adenosine amine congener. Thus, modulation of neuroinflammation by the A1AR represents a novel mechanism that provides new therapeutic opportunities for MS and other demyelinating diseases.
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 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) | 0.001 | 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".