Adenosine stimulates depolarization and rise in cytoplasmic [Ca<sup>2+</sup>] in type I cells of rat carotid bodies
Bibliographic record
Abstract
During hypoxia, the level of adenosine in the carotid bodies increases as a result of ATP catabolism and adenosine efflux via adenosine transporters. Using Ca2+ imaging, we found that adenosine, acting via A2A receptors, triggered a rise in cytoplasmic [Ca2+] ([Ca2+]i) in type I (glomus) cells of rat carotid bodies. The adenosine response could be mimicked by forskolin (but not its inactive analog), and could be abolished by the PKA inhibitor H89. Simultaneous measurements of membrane potential (perforated patch recording) and [Ca2+]i showed that the adenosine-mediated [Ca2+]i rise was accompanied by depolarization. Ni2+, a voltage-gated Ca2+ channel (VGCC) blocker, abolished the adenosine-mediated [Ca2+]i rise. Although adenosine was reported to inhibit a 4-aminopyridine (4-AP)-sensitive K+ current, 4-AP failed to trigger any [Ca2+]i rise, or to attenuate the adenosine response. In contrast, anandamide, an inhibitor of the TWIK-related acid-sensitive K+-1 (TASK-1) channels, triggered depolarization and [Ca2+]i rise. The adenosine response was attenuated by anandamide but not by tetraethylammonium. Our results suggest that adenosine, acting via the adenylate cyclase and PKA pathways, inhibits the TASK-1 K+ channels. This leads to depolarization and activation of Ca2+ entry via VGCC. This excitatory action of adenosine on type I cells may contribute to the chemosensitivity of the carotid body during hypoxia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".