Anti-atherogenic properties of Kgengwe (<i>Citrullus lanatus</i>) seed powder in low-density lipoprotein receptor knockout mice are mediated through beneficial alterations in inflammatory pathways
Bibliographic record
Abstract
Kgengwe fruits are commonly consumed in sub-Saharan countries. Recent reports indicated low coronary artery disease rates in those regions. To investigate anti-atherogenic properties and potential mechanisms of action of Kgengwe seed powder (KSP), male low-density lipoprotein receptor knockout (LDL-r-KO) mice were fed with an atherogenic diet supplemented with (treated, n = 10) or without (controls, n = 10) 10% (w/w) KSP for 20 weeks. Proximate analysis revealed that KSP contained 38% fibre and 15% lipids. KSP supplementation was not associated with significant changes in body weight gain rate, food intake, and plasma lipid levels. However, the average atherosclerotic lesion size in the aortic roots in the KSP-treated group was 58% smaller than that in the control group (0.26 vs 0.11 mm2, p < 0.05). This strong anti-atherogenic effect was associated with significant increases in the average plasma levels of certain cytokines such as IL-10 (6 vs 13 pg/mL, p < 0.05), GM-CSF (0.1 vs 0.2 pg/mL, p < 0.05), and EPO (7 vs 16 pg/mL, p < 0.05) along with reductions in the average levels of plasma MCP-1 (19 vs 14 pg/mL, p < 0.05) and MIP-2 (28 vs 13 pg/mL, p < 0.05). Except for relatively high levels of saturated fatty acids, KSP possesses balanced nutrient compositions with strong anti-atherogenic properties, which may be mediated through alterations in inflammatory pathways. Additional studies warrant confirmation and mechanism(s) of action of such effects. Novelty: Kgengwe seeds prevent atherogenesis in LDL-r-KO mice. Kgengwe seeds increase circulating levels of IL-10 and EPO. No reduction in plasma total cholesterol levels.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".