Plasma and fecal bioactive mediators in relation to the prevention of atherogenesis in LDL-r-KO mice: insights from an African staple food
Bibliographic record
Abstract
Our previous study revealed a significant anti-atherosclerotic effect of Kgengwe seed powder (KSP) in low-density lipoprotein receptor knockout (LDL-r-KO) mice. The importance of various lipid and protein metabolites, including certain amino acids and fatty acids on atherogenesis has been well established. Thus, we used plasma and fecal samples from our previous study to further study the association of such metabolites with atherosclerotic lesion development. Male LDL-r-KO mice were provided with an atherogenic diet supplemented with (treated, n = 10) or without (controls, n = 10) 10% ( w/ w) KSP for 20 weeks. The treated group showed significantly ( P < 0.05) higher plasma levels of many amino acids plus propionic acid, indoleacetic acid, pyruvic acid, beta-hydroxybutyric acid, alpha-ketoglutaric acid, trimethylamine N-oxide, LYSOC16:0, LYSOC18:0, and LYSOC18:2, as compared with those of the control group. Similarly, several oxylipins, including 15-keto prostaglandin E2, 9,10,13-trihydroxy-octadecenoic acid, 9,10-epoxy-octadecenoic acid, and 12,13-epoxy-octadecenoic acid increased by approximately 2.0 log2 folds ( P < 0.05) in the plasma of the treated group. Other oxylipins, including 15,16-epoxy-octadecadieonic acid, 13-hydroxy-octadecadienoic acid, and prostaglandin E2 showed also an increased level, but to a lesser extent. Furthermore, our findings showed a significant positive correlation between plasma concentrations of prostaglandin E2 and IL-10 in the treated mice. We also observed a significant negative association between atherosclerotic lesion size and plasma levels of citrulline, lysine, alpha-ketoglutaric acid, and 15,16 epoxy-octadecadienoic acid. Additional in vitro and in vivo studies are needed to explore the mechanisms of such associations.
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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) | 0.001 | 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 | 0.001 | 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".