Serum metabonomic analysis of apoE−/− mice reveals progression axes for atherosclerosis based on NMR spectroscopy
Bibliographic record
Abstract
Atherosclerosis is a multifactorial and progressive disease commonly correlated with a high fat diet. The aim of this study was to identify potential biomarkers for the early diagnosis and monitoring of the progression of atherogenesis in apoE(-/-) mice using (1)H NMR-based metabonomics. The apoE(-/-) mice were split into four groups according to the duration of high fat feeding (0 w, 2 w, 4 w and 8 w), and each group possessed different pathological characteristics. Serum (1)H NMR-based metabonomics selectively captured the metabotypes that correlated with the degree of atherosclerosis, showing a time-dependent progression from the physiological to pathophysiological status. It was noted that changes in HDL, choline, taurine, glycine and glucose may be regarded as specific biomarkers of the early stage of atherosclerosis. With the progression of atherosclerosis, disorders in the metabolism of amino acids such as valine, alanine and methionine appeared when large atherosclerotic plaques existed. Multiple biochemical disorders involving lipid metabolism, energy and fatty acid metabolism were observed in the progression of atherosclerosis in apoE(-/-) mice. This study demonstrated that (1)H NMR-based metabonomics can provide biochemical information about the progression of atherogenesis and offer a non-invasive means to discover potential biomarkers for the onset and development of atherosclerosis.
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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 | 0.001 | 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".