Extrapolating microdomain Ca2+ dynamics using BK channels as a Ca2+ sensor
Bibliographic record
Abstract
Ca(2+) ions play crucial roles in mediating physiological and pathophysiological processes, yet Ca(2+) dynamics local to the Ca(2+) source, either from influx via calcium permeable ion channels on plasmic membrane or release from internal Ca(2+) stores, is difficult to delineate. Large-conductance calcium-activated K(+) (BK-type) channels, abundantly distribute in excitable cells and often localize to the proximity of voltage-gated Ca(2+) channels (VGCCs), spatially enabling the coupling of the intracellular Ca(2+) signal to the channel gating to regulate membrane excitability and spike firing patterns. Here we utilized the sensitivity and dynamic range of BK to explore non-uniform Ca(2+) local transients in the microdomain of VGCCs. Accordingly, we applied flash photolysis of caged Ca(2+) to activate BK channels and determine their intrinsic sensitivity to Ca(2+). We found that uncaging Ca(2+) activated biphasic BK currents with fast and slow components (time constants being τf ≈ 0.2 ms and τs ≈ 10 ms), which can be accounted for by biphasic Ca(2+) transients following light photolysis. We estimated the Ca(2+)-binding rate constant kb (≈1.8 × 10(8) M(-1) s(-1)) for mSlo1 and further developed a model in which BK channels act as a calcium sensor capable of quantitatively predicting local microdomain Ca(2+) transients in the vicinity of VGCCs during action potentials.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".