Heparanase Shakes Hands With Lipoprotein Lipase: A Tale of Two Cells
Bibliographic record
Abstract
Altered metabolism with excess provision of lipid substrates may be a major pathogenetic factor linking diabetes with cardiovascular disease—lipoprotein lipase (LPL) being one facilitator of such a process (1,2). Hyperlipidemia, by itself, plays a major role in lipid-provoked cardiovascular pathologies, largely mediated through LPL (3). Under physiological state, due to their presence on the endothelial cell surface, LPLs break down triglyceride lipoproteins in the circulation and provide tissues, like heart and skeletal muscle, with the required fatty acid substrates to derive ATP (3,4). A large volume of data, using genetically manipulated animal models and clinical studies, has found that LPL homeostasis is required for normal cardiac metabolism and function (5,6). In diabetes, increased metabolic demand of the heart is met by the breakdown of fatty acids by coronary LPL. LPL is produced by cardiomyocytes, which need endothelial-derived heparanase for LPL production. In this issue, Wang et al. (7) demonstrated that endothelial heparanase is taken up by the cardiomyocytes through caveolae and is converted to an active form in the lysosomal compartments of these cells. Endothelium-derived heparanase is instrumental for cleaving and releasing LPL from the heparan sulfate proteoglycans on the cardiomyocyte cell surface. Activated heparanase further translocates in the nucleus, increases histone acetylation, and augments matrix metalloproteinase-9 (MMP-9) production. Although this is an adaptive mechanism, increased MMP-9 may …
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".