Oxidized LDL enhances stretch-induced smooth muscle cell proliferation through alterations in nuclear protein import
Bibliographic record
Abstract
Mechanical stress contributes to hypertension and atherosclerosis partly through the stimulation of vascular smooth muscle cell (VSMC) proliferation. Oxidized low density lipoprotein (oxLDL) is another important atherogenic factor that can increase VSMC proliferation. The purpose of this study was to investigate whether oxLDL could further enhance the proliferative action of mechanical stretch on VSMC, and to determine the mechanism responsible for this interaction. Because nuclear protein import is critical in regulating gene expression, transcription, and cell proliferation, its involvement in the mitogenic effects of oxLDL and mechanical stress was studied. OxLDL enhanced the proliferative effects of mechanical stretch on its own in rabbit aortic VSMC, and induced increases in the expression of HSP60 in an additive manner. Adenoviral-mediated overexpression of HSP60 induced increases in cell proliferation compared with uninfected VSMC. Mechanical stretch and oxLDL stimulated the rate of nuclear protein import in VSMC and increased the expression of nucleoporins. These effects were sensitive to inhibition of the MAPK pathway. We conclude that oxLDL and mechanical stretch have a synergistic effect on VSMC proliferation. This synergistic effect is induced through a stimulation of nuclear protein import via HSP60 and an activation of the MAPK pathway.
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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".