Docosahexaenoic acid induces ciap1 mRNA and protects human endothelial cells from stress-induced apoptosis
Bibliographic record
Abstract
Induction of apoptosis represents a potential reaction of endothelial cells (ECs) after injury of the vascular endothelium. Beneficial effects of n-3 polyunsaturated fatty acids (PUFAs) in vascular diseases are widely recognized although the responsible mechanisms are not fully understood. Because it is not known whether PUFAs modulate EC apoptosis, we investigated the effects of n-3 and n-6 PUFAs on 4-hydroxynonenal (HNE)-induced EC apoptosis by annexin V staining and caspase-3 activation assays. Pretreatment with the n-3 fatty acid docosahexaenoic acid (DHA) reduced HNE-induced EC apoptosis. DHA-treated cells did not show the pronounced drop in intracellular GSH after HNE exposure seen in vehicle- or n-6 arachidonic acid-treated cells. This is most likely due to increased GSH levels in DHA-treated cells. Furthermore, DHA pretreatment increased ciap1 mRNA levels and transfection of cIAP1 small interfering RNA abolished the protective effect of DHA in HNE-induced apoptosis in HUVECs. Thus pretreatment of HUVECs with DHA reduces HNE-induced oxidative stress and apoptosis, and the protective effects of DHA seem to be dependent on cIAP1. The results provide a possible new mechanism for the atheroprotective effects of n-3 fatty acids in vascular disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".