Role of NKT cell activation in malondialdehyde-modified low density lipoprotein immunization for atherosclerosis prevention
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease involving lipid accumulation in the arterial wall and is currently the top cause of death in Western countries. Currently available therapies involving risk factor modifications have been proven effective, but clinical trials show that this approach only leads to a 40% relative risk reduction. Therefore, new therapies targeting the disease process are needed to improve the efficacy of current atherosclerosis treatment. In vivo studies have shown that immunization with disease associated antigens such as malondialdehyde-modified LDL (MDA-LDL) reduces atherosclerosis progression through induction of protective antibodies. Despite these findings, translation into human use have been difficult for numerous reasons with one being difference in the adjuvant used between humans and animals. Therefore, in vivo immunization studies using adjuvants that can also be used safely for humans are needed to better predict human response from animal data. In this thesis, MDA-LDL immunization studies using α-galactosylceramide (α-GalCer) as an adjuvant aimed for atherosclerosis prevention were done. α-GalCer is a glycolipid that has been shown to be safe for human use and it activates immunomodulatory NKT cells which recognize glycolipid antigens presented by CD1d molecules. We demonstrate that co-injection of α-GalCer with MDA-LDL results in enhanced antibody response, plasma cell formation and induction of antigen-specific Th2 response. This effect was NKT cell and CD4+ T cell dependent but independent of hyperlipidemia. Importantly, use of α-GalCer adjuvant resulted in a 40% reduction in lesion size compared to controls and the extent of reduction was comparable to immunizations including adjuvants used in previous animal studies. Our results demonstrate that α-GalCer can be used as a vaccine adjuvant for atherosclerosis prevention and may likely be one adjuvant candidate for clinical studies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".