Proliferative Effect of Lipoprotein Lipase on Human Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Vascular smooth muscle cell (VSMC) proliferation is a key event in the development and progression of atherosclerotic lesions. Accumulating evidence suggests that lipoprotein lipase (LPL) produced in the vascular wall may exert proatherogenic effects. The aim of the present study was to examine the effect of LPL on VSMC proliferation. Incubation of growth-arrested human VSMCs with purified endotoxin-free bovine LPL for 48 and 72 hours, in the absence of any added exogenous lipoproteins, resulted in a dose-dependent increase in VSMC growth. Addition of VLDLs to the culture media did not further enhance the LPL effect. Treatment of growth-arrested VSMCs with purified human or murine LPL (1 microg/mL) led to a similar increase in cell proliferation. Neutralization of bovine LPL by the monoclonal 5D2 antibody, irreversible inhibition, or heat inactivation of the lipase suppressed the LPL stimulatory effect on VSMC growth. Moreover, preincubation of VSMCs with the specific protein kinase C inhibitors calphostin C and chelerythrine totally abolished LPL-induced VSMC proliferation. In LPL-treated VSMCs, a significant increase in protein kinase C activity was observed. Treatment of VSMCs with heparinase III (1 U/mL) totally inhibited LPL-induced human VSMC proliferation. Taken together, these data indicate that LPL stimulates VSMC proliferation. LPL enzymatic activity, protein kinase C activation, and LPL binding to heparan sulfate proteoglycans expressed on VSMC surfaces are required for this effect. The stimulatory effect of LPL on VSMC proliferation may represent an additional mechanism through which the enzyme contributes to the progression of atherosclerosis.
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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) | 0.002 | 0.001 |
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".