Recalibrating vascular malformations and mechanotransduction by pharmacological intervention
Bibliographic record
Abstract
Circulation of blood throughout the cardiovascular system results in biomechanical forces that profoundly influence vessel development and maintenance. Fluid shear stress along the inner lining of blood vessels imparts mechanical forces upon vascular cells. Endothelial cells (ECs), which line all blood and lymphatic vessels, are exquisitely equipped to sense mechanical forces and to transduce these stimuli into biochemical signals, which control their proliferation, migration, cytoskeleton organization, and cell-cell adhesion. Vascular malformations can arise when patterns of blood flow change or when vascular cells develop a disturbed response to hemodynamic forces due to genetic mutations. For instance, genetic evidence suggests that mutations causing dysregulated RAS/MAPK and nitric oxide (NO) signaling within vascular cells lead to malformations and dysplasias. The influence of biomechanical cues on genetically vulnerable cells provides a promising therapeutic avenue. Vascular malformations are often incurable, and patients experience lifelong chronic pain, disfigurement, or even premature death. A major obstacle to developing useful therapies is our poor understanding of the molecular underpinnings of these vascular defects. Tangible therapeutic approaches may be based on the novel idea that vascular cell responses to blood flow can be normalized using pharmacological modulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 teacher head, 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".