Mechanotransduction by endothelial cells is locally generated, direction‐dependent, and ligand‐specific
Bibliographic record
Abstract
Vascular endothelial cells display a wide panel of responses to changes in the shear stress that is exerted on them by blood flow. How sensory mechanisms convey information about flow conditions and how this information is integrated remains poorly understood. The issue is confounded by: (1) a large number of potential force sensors, (2) difficulties in differentiating these sensors from downstream sites of signal integration, and (3) the complexities inherent in understanding how forces are transmitted from the apical surface of the cell via the cytoskeleton to intracellular sites. As a consequence, neither the structures that sense force nor the nature of the forces that loads them have been clearly defined. In this study, we employed magnetic microspheres coated with ligands that bind integrin subsets (RGD peptides or type I collagen) or PECAM-1 to discriminate the downstream signaling effects of different sensor molecules and mechanisms for how they are loaded. We found that application of force to these transmembrane molecules elicited biologically important signaling (ERK1/2, AKT, and GSK-3beta phosphorylation), and downstream biological responses that depended on the following two factors: (1) the ligand that transmitted force and (2) the direction in which force was applied. These findings indicate that ligands locally generate different shear-induced responses in endothelium that depend on how force is delivered.
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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".