Sarcoplasmic Reticulum-Sarcolemma Interactions and Vascular Smooth Muscle Tone
Bibliographic record
Abstract
A characteristic of vascular smooth muscle cell morphology is a close apposition of its peripheral sarcoplasmic reticulum (SR) with the sarcolomma; this arrangement gives rise to important functional interactions whereby the peripheral SR regulates Ca2+ influx and vascular tone. We review here the key evidence supporting the following aspects of SR-sarcolemma interactions while establishing a conceptual framework encompassing (i) the SR ultrastructure and functions, (ii) the integration of the sarcolemmal Na+-Ca2+ exchanger and the peripheral SR in the mediation of a bidirectional Ca2+ exchange between the peripheral SR and the extracellular space, (iii) the existence of a higher myoplasmic free Ca2+ concentration [Ca2+]myo in the subsarcolemmal space formed between the sarcolemma and the peripheral SR relative to the [Ca2+]myo of the inner myoplasm in the resting smooth muscle cell, (iv) the division of the subsarcolemmal space into functional microdomains, (v) the existence of spontaneous localized bursts of Ca2+ release from the peripheral SR (Ca2+ sparks) towards the sarcolemma, (vi) the physiological triggering of nonlocalized Ca2+ release from the peripheral SR by Ca2+ influx (Ca2+-induced Ca2+ release), and (vii) capacitative Ca2+ entry in vascular smooth muscle. We present an overview of the physiological and pathological implications of these interactions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".