Oxidized LDL triggers changes in oxidative stress and inflammatory biomarkers in human macrophages
Bibliographic record
Abstract
Oxidized low-density lipoprotein (oxLDL) is a well-recognized proatherogenic particle that functions in atherosclerosis . In this study, we established conditions to generate human oxLDL, characterized according to the grade of lipid and protein oxidation , particle size and oxylipin content. The induction effect of the cellular proatherogenic response was assessed in foam cells by using an oxLDL-macrophage interaction model. Uptake of oxLDL, reactive oxygen species production and expression of oxLDL receptors (CD36, SR-A and LOX-1) were significantly increased in THP-1 macrophages. Analyses of 35 oxylipins revealed that isoprostanes (IsoP) and prostaglandins (PGs) derived from the oxidation of arachidonic, dihomo gamma-linolenic and eicosapentaenoic acids were strongly and significantly induced in macrophages stimulated with oxLDL. Importantly, the main metabolites responsible for the THP1-macrophage response to oxLDL exposure were the oxidative stress markers 5- epi -5-F 2t -IsoP, 15-E 1t -IsoP, 8-F 3t -IsoP and 15-keto-15-F 2t -IsoP as well as inflammatory markers PGDM, 17- trans -PGF 3α , and 11β-PGF 2α , all of which are reported here, for the first time, to function in the interaction of oxLDL with THP-1 macrophages. By contrast, a salvage pathway mediated by anti-inflammatory PGs (PGE 1 and 17- trans -PGF 3α ) was also identified, suggesting a response to oxLDL-induced injury . In conclusion, when THP-1 macrophages were treated with oxLDL, a specific induction of biomarkers related to oxidative stress and inflammation was triggered. This work contributes to our understanding of initial atherogenic events mediated by oxLDL-macrophage interactions and helps to generate new approaches for their modulation.
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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".