Oxidized Low-Density Lipoprotein Retards the Growth of Proliferating Cells by Inhibiting Nuclear Translocation of Cell Cycle Proteins
Bibliographic record
Abstract
OBJECTIVE: Our study tested the hypothesis that the mitogenic effect of oxidized low-density lipoprotein (oxLDL) on vascular cells may be further enhanced by the presence of cytokines and growth factors known to be present in the atherosclerotic environment. METHODS AND RESULTS: Quiescent fibroblasts and vascular smooth muscle cells were treated with 10 or 50 microg/mL minimally-oxidized LDL in combination with serum for 24 or 48 hours. Surprisingly, these cells showed inhibited release from growth arrest and a significant reduction in the number of cells completing the cell cycle when compared with cells treated with serum alone. This was not due to an induction of apoptosis. The antiproliferative effects were not closely associated with changes in the expression of cell cycle proteins. Instead, oxLDL inhibited the translocation of cell cycle proteins cell division cycle (Cdc) 2, cyclin-dependent kinase (Cdk) 2, Cdk 4, Cyclin A, Cyclin B1, Cyclin D1, and proliferative cell nuclear antigen (PCNA) into the nucleus, as compared with separate treatments with serum alone. Kinase activation associated with specific cell cycle proteins was also inhibited by oxLDL. CONCLUSIONS: oxLDL, in the presence of serum, has a surprising inhibitory effect on cell proliferation that occurs through an inhibition of import of cell cycle proteins into the cell nucleus.
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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".