Seeing is believing! Imaging Ca<sup>2+</sup>‐signalling events in living cells
Bibliographic record
Abstract
Ever since it was shown that maintenance of muscle contraction required the presence of extracellular Ca(2+), evidence has accumulated that Ca(2+) plays a crucial role in excitation-contraction coupling. This culminated in the use of the photoprotein aequorin to demonstrate that [Ca(2+)](i) increased after depolarization but before contraction in barnacle muscle. Green fluorescent protein was extracted from the same jellyfish as aequorin, so this work also has important historical links to the use of fluorescent proteins as markers in living cells. The subsequent development of cell-permeant Ca(2+) indicators resulted in a dramatic increase in related research, revealing Ca(2+) to be a ubiquitous cell signal. High-speed, confocal Ca(2+) imaging has now revealed subcellular detail not previously apparent, with the identification of Ca(2+) sparks. These act as building blocks for larger transients during excitation-contraction coupling in cardiac muscle, but their function in smooth muscle appears more diverse, with evidence suggesting both 'excitatory' and 'inhibitory' roles. Sparks can activate Ca(2+)-sensitive Cl() and K(+) currents, which exert positive and negative feedback, respectively, on global Ca(2+) signalling, through changes in membrane potential and activation of voltage-operated Ca(2+) channels. Calcium imaging has also demonstrated that agonists that appear to evoke relatively tonic increases in average [Ca(2+)](i) at the whole tissue level often stimulate much higher frequency phasic Ca(2+) oscillations at the cellular level. These findings may require re-evaluation of some of our models of Ca(2+) signalling to account for newly revealed cellular and subcellular detail. Future research in the field is likely to make increasing use of genetically coded Ca(2+) indicators expressed in an organelle- or tissue-specific manner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".