Force-Induced Polarized Mitosis of Endothelial and Smooth Muscle Cells in Arterial Remodeling
Bibliographic record
Abstract
Arteries display highly directional growth and remodeling that are specific to increases in the mechanical loads imposed on them by blood pressure, blood flow, and lengthwise tensile forces that are transmitted from the tissues to which they are attached. This study examined the effect of mechanical forces on the direction in which mitosis delivers daughter cells, as a mechanism for directional growth. Lateral forces were imposed on surface integrins of cultured endothelial cells by seeding the cells with arginine-glycine-aspartate peptide-coated magnetic microspheres and applying a magnetic field. Video images revealed that the mitotic axis of dividing cells became highly biased in the direction of applied force. Distribution of cortactin, which participates in polarized mitoses driven by other stimuli, was highly sensitive to mechanical loading and interfering with cortactin function arrested cell growth. Smooth muscle cell mitoses also proved to be sensitive to mechanical force: when lengthwise force imposed on rabbit carotid arteries was altered by excision of a vessel segment and reanastomosis of the cut ends, direction of mitosis was dramatically altered. These findings indicate that influences of mechanical force can modulate the manner in which mitosis of vascular cells contributes to reorganization of arterial wall tissue.
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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".