Shear stress, arterial identity and atherosclerosis
Bibliographic record
Abstract
In the developing embryo, the vasculature first takes the form of a web-like network called the vascular plexus. Arterial and venous differentiation is subsequently guided by the specific expression of genes in the endothelial cells that provide spatial and temporal cues for development. Notch1/4, Notch ligand delta-like 4 (Dll4), and Notch downstream effectors are typically expressed in arterial cells along with EphrinB2, whereas chicken ovalbumin upstream promoter transcription factor II (COUP-TFII) and EphB4 characterise vein endothelial cells. Haemodynamic forces (blood pressure and blood flow) also contribute importantly to vascular remodelling. Early arteriovenous differentiation and local blood flow may hold the key to future inflammatory diseases. Indeed, despite the fact that atherosclerosis risk factors such as smoking, hypertension, hypercholesterolaemia, and diabetes all induce endothelial cell dysfunction throughout the vasculature, plaques develop only in arteries, and they localise essentially in vessel branch points, curvatures and bifurcations, where blood flow (and consequently shear stress) is low or oscillatory. Arterial segments exposed to high blood flow (and high laminar shear stress) tend to remain plaque-free. These observations have led many to investigate what particular properties of arterial or venous endothelial cells confer susceptibility or protection from plaque formation, and how that might interact with a particular shear stress environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".