The 3D structure of the myoendothelial projections: intracellular organelles, protein trafficking and biological function (677.12)
Bibliographic record
Abstract
The 3‐D structure of the myoendothelial projections: intracellular organelles, protein trafficking and biological Function Nadia Maarouf and Donald G. Welsh Department of physiology and pharmacology, University of Calgary, Calgary, AB, Canada Endothelial and smooth muscle cells communicate with one another in order to regulate vessel diameter. A key element of heterocellular communication is the myoendothelial projection, a thin endothelial extension that crosses the internal elastic lamina to make contact with the smooth muscle. The aim of this study was to ascertain the structural composition of the myoendothelial projection using electron tomography, a technique that enables the generation of 3‐D models. Anatomical models from mesenteric arteries reveal that myoendothelial projections are generally devoid of membranous structures, contrary to current thought. Intriguingly, key organelles such as endoplasmic reticulum with ribosomes, caveolae and vesicles were found in abundance at the base of the projection. The presence and positioning of the ribosomal endoplasmic reticulum suggests that in addition to governing Ca 2+ regulation, this organelle contributes to protein packaging and trafficking to the plasma membrane which may include ion channels. In summary, the electron tomography approach provides valuable structural insight into the detailed composition of myoendothelial projections and their potential role in arterial tone regulation. Grant Funding Source : CIHR
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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) | 0.004 | 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".