Human Smooth Muscle Cell Subpopulations Differentially Accumulate Cholesteryl Ester When Exposed to Native and Oxidized Lipoproteins
Bibliographic record
Abstract
BACKGROUND: Vascular smooth muscle cells (SMCs) manifest diverse phenotypes and emerging evidence suggests this is caused by inherently distinct SMC subtypes. Recently, Li et al (Circ Res 2001;89:517-525) successfully cloned 2 uniquely responsive SMC subpopulations from a single human artery and we used this unique resource to test the hypothesis that distinct SMC subtypes are differential precursors of foam cell formation. METHODS AND RESULTS: When challenged with human atherogenic native or oxidized hypertriglyceridemic very-low-density lipoprotein (HTG-VLDL), the larger, slower-growing, spindle-shaped HITB5 SMC clone accumulated significantly more cholesteryl ester (CE) and triglyceride (TG) than the smaller, faster-growing epithelioid-shaped HITA2 SMC clone (10 versus 2 microg CE/mg cell protein [PN] and 60 versus 7 microg TG/mg PN, P<0.05). Lipoprotein lipase (LPL), a key enzyme involved in lipoprotein uptake, was identified as one differentially expressed protein that altered the predisposition of HITA2 SMCs for lipid accumulation. Although HITB5 SMCs secreted significantly more LPL than did HITA2 SMCs (0.7 versus 0.2 U/mL media, P<0.05), the addition of bovine milk LPL to HITA2 SMCs, significantly increased native and oxidized HTG-VLDL-induced lipid accumulation. CONCLUSIONS: Inherently distinct SMC subsets are differentially predisposed to lipoprotein-induced lipid accumulation. Moreover, the environment can influence the response of SMC subsets to atherogenic lipoproteins.
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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".