Effects of glycated low-density lipoprotein on cell viability, proliferation, and growth factors of mouse embryo fibroblasts
Bibliographic record
Abstract
The predominant cause of death in diabetic patients is atherosclerotic coronary artery disease (CAD). Major gross cellular changes in the vascular wall of persons with CAD include endothelial injury and foam cell formation, as well as smooth muscle cell and fibroblast proliferation. This study examined the effects of glycated low density lipoprotein (glyLDL), a biochemical marker of diabetes, on cell viability, proliferation, and the expression of multiple growth factors in mouse embryo fibroblasts (MEF). The results demonstrated that exposure to ≥150 μg/mL of glyLDL for 24 h or 100 μg/mL of glyLDL for ≥48 h either significantly reduced cell viability or increased DNA fragmentation in MEF. GlyLDL treatment (25-100 μg/mL for up to 12 h) significantly increased the abundance of proliferating cell nuclear antigen (PCNA) and achieved a peak after 4 h exposure to glyLDL. Abundances of fibroblast growth factor-basic (FGF), transforming growth factor-β (TGF), and platelet-derived growth factor-A (PDGF) in MEF reached maximal levels after 2 h exposure to 50 μg/mL of glyLDL. The maximal increase of vascular endothelial growth factor (VEGF) was detected in MEF after 4 h of exposure to 50 μg/mL of glyLDL. Inhibitors for FGF (AZD4547), VEGF, or PDGF receptors (Axitinib), but not that for TGF receptor (LY364947), significantly decreased the abundance of (PCNA) in endothelial cells. The findings suggest that early exposure to a low dosage of glyLDL transiently increases the proliferation of MEF through the upregulation of FGF, VEGF, and (or) PDGF, and prolonged exposure to high concentrations of glyLDL reduced cell viability, which possibly accelerates atherogenesis under diabetic condition.
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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".