Cholesterol uptake capacity of HDL in culture medium of fresh primary human hepatocytes: an in vitro system for screening anti-atherosclerosis drugs focused on HDL functions
Bibliographic record
Abstract
OBJECTIVE: Removing excess cholesterol from atherosclerotic plaques is a crucial function of high-density lipoprotein (HDL). Compared to HDL cholesterol, cholesterol efflux capacity (CEC) is a better indicator of cardiovascular disease risk. However, this approach has several practical disadvantages, such as CEC assay requires cultured cells and takes several days to perform. Recently, we developed a simpler cell-free assay to assess the cholesterol uptake capacity (CUC), a new HDL functionality metric. In this study, we combined the HDL-CUC assay with PXB-cells LA, primary human hepatocytes derived from the humanized mouse liver, to investigate whether the CUC of HDL in the culture medium reflects the eicosapentaenoic acid (EPA) effects on HDL functionality. RESULTS: The CUC of HDL in the culture medium of PXB-cells LA was measured using the automated immunoassay system HI-1000. Adding EPA to the culture medium did not alter albumin or hepatic triglyceride lipase levels, confirming no significant EPA-induced damage to the hepatocytes. However, as reported for CEC, EPA significantly increased the CUC in a dose-dependent manner, highlighting the potential of EPA as a therapeutic candidate for patients with low CUC. Thus, the proposed assay system could be used for in vitro drug screening that improves HDL functionality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".