Calcium concentration changes during sensory transduction in spider mechanoreceptor neurons
Bibliographic record
Abstract
Most mechanoreceptor neurons encode mechanical signals into action potential trains within the same cell. Evidence suggests that intracellular calcium ion concentration, [Ca2+], increases during mechanotransduction, either by direct entry through mechanically activated channels or indirectly through voltage-activated calcium channels. However, little is known about the amounts of calcium involved or its roles in mechanotransduction. We estimated [Ca2+] in mechanoreceptor neurons of the spider, Cupiennius salei, during mechanical stimulation using Oregon Green BAPTA-1, and a single-compartment model of [Ca2+] as a function of action potential firing rate. Resting [Ca2+] was approximately 400 nM and increased to up to 2 microM at 30 action potentials/s. Similar levels of resting and stimulated [Ca2+] were obtained in the cell soma, axon and two parts of the sensory dendrite, including the region immediately adjacent to the site of sensory transduction. The time constant of rise and fall of [Ca2+] was 1-5 s in the dendrite and axon, but up to 15 s in the soma. Calcium elevation was dependent on action potentials and could not be induced by the receptor potential alone. Blockade of voltage-activated calcium channels by nickel ions prevented calcium increase, but thapsigargin, which empties intracellular calcium stores, had no effect. Estimates of calcium entry per action potential from fluorescence changes agreed approximately with estimates based on action potential voltage-time profile and previous reports of calcium channel properties. This first report of calcium levels during transduction in spiking mechanoreceptors suggests that calcium signaling plays important roles in primary somatosensory neurons.
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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) | 0.000 | 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".