Atherosclerotic plaque characterization by NMR spectroscopy
Bibliographic record
Abstract
Background: High resolution Magnetic Resonance Spectroscopy (MRS) in vitro was used to investigate the correlation between the chemical composition of atheromatous plaques and their stability (vulnerability to rupture) which is of fundamental importance and is a rather controversial point in literature. Methods: In this study five tissues were collected from ex vivo human abdominal aortas and divided in three groups according to their atherosclerotic lesion using the American Heart Association Committee on Vascular Lesions. Due to the semi-solid nature of the plaques, Magic Angle Spinning (MAS) and heteronuclear Decoupling (DEC) were used to acquire six 13 C spectra with appreciable high resolution for five samples and the solvent. The 1 H high resolution spectra also were obtained for the same samples. Results: The most common fatty acids present in the samples were identified and also the relative amounts of polyunsaturated (PUFA), unsaturated (UFA) and saturated fatty acids associated with the progress of the deterioration process of atheromatous plaques. Conclusions: NMR results show that the lipidic composition of atheromatous plaque evolves from a state with high levels of PUFA and UFA for a subject with lower classification to a new state containing, 6 and 4 times less unsaturated fatty acids respectively than those subjects with more stenotic lesions. In addition, average rate of conversion of PUFA to UFA is approximately two times higher in more stenotic lesions. These results suggest that the lipidic components of atheromatous plaque can be related to its vulnerability to rupture, which clearly demonstrate the importance of the method to study this 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 | 0.001 | 0.001 |
| 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".