In vivo visualization and attenuation of oxidized lipid accumulation in hypercholesterolemic zebrafish
Bibliographic record
Abstract
Oxidative modification of LDL is an early pathological event in the development of atherosclerosis. Oxidation events such as malondialdehyde (MDA) formation may produce specific, immunogenic epitopes. Indeed, antibodies to MDA-derived epitopes are widely used in atherosclerosis research and have been demonstrated to enable cardiovascular imaging. In this study, we engineered a transgenic zebrafish with temperature-inducible expression of an EGFP-labeled single-chain human monoclonal antibody, IK17, which binds to MDA-LDL, and used optically transparent zebrafish larvae for imaging studies. Feeding a high-cholesterol diet (HCD) supplemented with a red fluorescent lipid marker to the transgenic zebrafish resulted in vascular lipid accumulation, quantified in live animals using confocal microscopy. After heat shock-induced expression of IK17-EGFP, we measured the time course of vascular accumulation of IK17-specific MDA epitopes. Treatment with either an antioxidant or a regression diet resulted in reduced IK17 binding to vascular lesions. Interestingly, homogenates of IK17-EGFP-expressing larvae bound to MDA-LDL and inhibited MDA-LDL binding to macrophages. Moreover, sustained expression of IK17-EGFP effectively prevented HCD-induced lipid accumulation in the vascular wall, suggesting that the antibody itself may have therapeutic effects. Thus, we conclude that HCD-fed zebrafish larvae with conditional expression of EGFP-labeled oxidation-specific antibodies afford an efficient method of testing dietary and/or other therapeutic antioxidant strategies that may ultimately be applied to humans.
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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) | 0.001 | 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) | 0.001 | 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".