Quantification of Morphological Modulation, F-Actin Remodeling, and PECAM-1 (CD-31) Redistribution in Endothelial Cells in Response to Fluid-Induced Shear Stress Under Various Flow Conditions
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are the number one cause of death globally. Arterial endothelial cell (EC) dysfunction plays a key role in many of these CVDs, such as atherosclerosis. Blood flow-induced wall shear stress (WSS), among many other pathophysiological factors, is known to significantly contribute to EC dysfunction. The present study reports an in vitro investigation of the effect of quantified WSS on ECs, analyzing the EC morphometric parameters and cytoskeletal remodeling. The effects of four different flow cases (low steady laminar (LSL), medium steady laminar (MSL), nonzero-mean sinusoidal laminar (NZMSL), and laminar carotid (LCRD) waveforms) on the EC area, perimeter, shape index (SI), angle of orientation, F-actin bundle remodeling, and platelet endothelial cell adhesion molecule-1 (PECAM-1) localization were studied. For the first time, a flow facility was fully quantified for the uniformity of flow over ECs and for WSS determination (as opposed to relying on analytical equations). The SI and angle of orientation were found to be the most flow-sensitive morphometric parameters. A two-dimensional fast Fourier transform (2D FFT) based image processing technique was applied to analyze the F-actin directionality, and an alignment index (AI) was defined accordingly. Also, a significant peripheral loss of PECAM-1 in ECs subjected to atheroprone cases (LSL and NZMSL) with a high cell surface/cytoplasm stain of this protein is reported, which may shed light on of the mechanosensory role of PECAM-1 in mechanotransduction.
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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) | 0.000 | 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".