Lipid profiling in serum from apolipoprotein E-knock out mice fed with different diets and its application to the study of the regulatory effect on lipid metabolism
Bibliographic record
Abstract
Atherosclerosis is a chronic disease associated with oxidative stress and inflammatory activation and is the main underlying trigger for cardiovascular disease. There are many cardiovascular health products in the market; in order to evaluate the effect of these products, in this paper, a novel lipid profiling platform was established using the shot-gun mass spectrum method for the analysis of free fatty acid and phospholipids, and the high performance liquid chromatography coupled with mass spectroscopy method for the analysis of lipid mediators and triacylglycerol, respectively, in serum from male apolipoprotein E-knock out mice after different diet interventions. Changes in the four lipids above, and pathways and regulation of lipid metabolism in mice from different groups were further investigated. The result showed that all cardiovascular health products showed some certain potential to alleviate atherogenesis and ameliorate inflammation; among them, lemon apple cider vinegar drink and seal oil could significantly decrease triacylglycerol in mouse serum. The establishment of this lipidome profiling platform helps to better understand the metabolism regulation and intervention mechanism of different cardiovascular health products in chronic diseases, such as atherogenesis. This platform could be applied to other cardiovascular health products and provide reliable lipid profiling data for their potential effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".