Activation of sarcolemma and nuclear membranes ET-1 receptors regulates transcellular calcium levels in heart and vascular smooth muscle cells
Bibliographic record
Abstract
The use of an ET-1 fluorescent probe in human heart and vascular smooth muscle cells showed that ET-1 receptors are present at both the sarcolemma and nuclear envelope membranes. The use of immunofluorescence studies showed that the ETA receptor was mainly present at the sarcolemma and cytosolic levels. However, the ETB receptor was present at the sarcolemma and the cytosol, as well as the nuclear envelope membranes and the nucleoplasm. In addition, ET-1 immunoreactivity was seen in the cytosol and the nucleus. Using Ca2+ fluorescent probes such as Fluo-3, Indo 1, and yellow cameleon, as well as confocal microscopy three-dimensional image measurement technique, stimulation of ET-1 receptors at the sarcolemma membranes induced an increase of cytosolic and nuclear free Ca2+ levels. This effect of extracellular ET-1 was blocked by removal of extracellular calcium. Direct stimulation of ET-1 receptors at the nuclear envelope membranes also induced an increase of intranuclear free Ca2+ level. Our results suggest that the stimulation of sarcolemmal Ca2+ influx by ET-1 seems to be due to the activation of ETA and ETB receptors. However, the increase of nucleoplasmic Ca2+ levels by cytosolic ET-1 seems to be mediated via the activation of ETB receptors. Activation of nuclear membranes ETB receptors seems to prevent nuclear Ca2+ overload and may protect the cell from apoptosis.
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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".