Physical contact between human vascular endothelial and smooth muscle cells modulates cytosolic and nuclear calcium homeostasis
Bibliographic record
Abstract
The interaction between vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs) plays an important role in the modulation of vascular tone. There is, however, no information on whether direct physical communication regulates the intracellular calcium levels of human VECs (hVECs) and (or) human VSMCs (hVSMCs). Thus, the objective of the study is to verify whether co-culture of hVECs and hVSMCs modulates cytosolic ([Ca2+]c) and nuclear calcium ([Ca2+]n) levels via physical contact and (or) factors released by both cell types. Quantitative 3D confocal microscopy for [Ca2+]c and [Ca2+]n measurement was performed in cultured hVECs or hVSMCs or in co-culture of hVECs–hVSMCs. Our results show that: (1) physical contact between hVECs–hVECs or hVSMCs–hVSMCs does not affect [Ca2+]c and [Ca2+]n in these 2 cell types; (2) physical contact between hVECs and hVSMCs induces a significant increase only of [Ca2+]n of hVECs without affecting the level of [Ca2+]c and [Ca2+]n of hVSMCs; and (3) preconditioned culture medium of hVECs or hVSMCs does not affect [Ca2+]c and [Ca2+]n of both types of cells. We concluded that physical contact between hVECs and hVSMCs only modulates [Ca2+]n in hVECs. The increase of [Ca2+]n in hVECs may modulate nuclear functions that are calcium dependent.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".