Granule matrix property and rapid “kiss-and-run” exocytosis contribute to the different kinetics of catecholamine release from carotid glomus and adrenal chromaffin cells at matched quantal size
Bibliographic record
Abstract
Catecholamine-containing small dense core granules (SDCGs, vesicular diameter of ~100 nm) are prominent in carotid glomus (chemosensory) cells and some neurons, but the release kinetics from individual SDCGs has not been studied in detail. In this study, we compared the amperometric signals from glomus cells with those from adrenal chromaffin cells, which also secrete catecholamine but via large dense core granules (LDCGs, vesicular diameter of ~200-250 nm). When exocytosis was triggered by whole-cell dialysis (which raised the concentration of intracellular Ca(2+) ([Ca(2+)](i)) to ~0.5 µmol/L), the proportion of the type of signal that represents a flickering fusion pore was 9-fold higher for glomus cells. Yet, at the same range of quantal size (Q, the total amount of catecholamine that can be released from a granule), the kinetics of every phase of the amperometric spike signals from glomus cells was faster. Our data indicate that the last phenomenon involved at least 2 mechanisms: (i) the granule matrix of glomus cells can supply a higher concentration of free catecholamine during exocytosis; (ii) a modest elevation of [Ca(2+)](i) triggers a form of rapid "kiss-and-run" exocytosis, which is very prevalent among glomus SDCGs and leads to incomplete release of their catecholamine content (and underestimation of their Q value).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".