Selective oxidative stress and cholesterol metabolism in lipid‐metabolizing cell classes: Distinct regulatory roles for pro‐oxidants and antioxidants
Bibliographic record
Abstract
Atherogenesis is associated with macrophage cholesterol and oxidized lipids accumulation and foam cell formation. However, two other major lipid-metabolizing cell classes, namely intestinal and liver cells, are also associated with atherogenesis. This study demonstrates that manipulations of cellular oxidative stress (by fatty acids, glucose, low-density lipoprotein, angiotensin II, polyphenolic antioxidants, or the glutathione/paraoxonase 1 systems) have some similar, but also some different effects on cholesterol metabolism in macrophages (J774A.1) versus intestinal cells (HT-29) versus liver cells (HuH7). Cellular oxidative stress was ≈3.5-folds higher in both intestinal and liver cells versus macrophages. In intestinal cells or liver cells versus macrophages, the cholesterol biosynthesis rate was increased by 9- or 15-fold, respectively. In both macrophages and intestinal cells C-18:1 and C-18:2 but not C-18:0, fatty acids significantly increased oxidative stress, whereas in liver cells oxidative stress was significantly decreased by all three fatty acids. In liver cells, trans C-18:1 versus cis C-18:1, unlike intestinal cells or macrophages, significantly increased cellular oxidative stress and cellular cholesterol biosynthesis rate. Pomegranate juice (PJ), red wine, or their phenolics gallic acids or quercetin significantly reduced cellular oxidation mostly in macrophages. Recombinant PON1 significantly decreased macrophage (but not the other cells) oxidative stress by ≈30%. We conclude that cellular atherogenesis research should look at atherogenicity, not only in macrophages but also in intestinal and liver cells, to advance our understanding of the complicated mechanisms behind atherogenesis. © 2015 BioFactors, 41(4):273-288, 2015.
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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".