The NF-κB signal transduction pathway in aortic endothelial cells is primed for activation in regions predisposed to atherosclerotic lesion formation
Bibliographic record
Abstract
Atherosclerotic lesions form at distinct sites in the arterial tree, suggesting that hemodynamic forces influence the initiation of atherogenesis. If NF-kappaB plays a role in atherogenesis, then the activation of this signal transduction pathway in arterial endothelium should show topographic variation. The expression of NF-kappaB/IkappaB components and NF-kappaB activation was evaluated by specific antibody staining, en face confocal microscopy, and image analysis of endothelium in regions of mouse proximal aorta with high and low probability (HP and LP) for atherosclerotic lesion development. In control C57BL/6 mice, expression levels of p65, IkappaBalpha, and IkappaBbeta were 5- to 18-fold higher in the HP region, yet NF-kappaB was activated in a minority of endothelial cells. This suggested that NF-kappaB signal transduction was primed for activation in HP regions on encountering an activation stimulus. Lipopolysaccharide treatment or feeding low-density lipoprotein receptor knockout mice an atherogenic diet resulted in NF-kappaB activation and up-regulated expression of NF-kappaB-inducible genes predominantly in HP region endothelium. Preferential regional activation of endothelial NF-kappaB by systemic stimuli, including hypercholesterolemia, may contribute to the localization of atherosclerotic lesions at sites with high steady-state expression levels of NF-kappaB/IkappaB components.
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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 | 0.001 |
| 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".