Anti-Atherosclerotic Properties of Wild Rice in Low-Density Lipoprotein Receptor Knockout Mice: The Gut Microbiome, Cytokines, and Metabolomics Study
Bibliographic record
Abstract
BACKGROUND AND AIM: We previously reported the anti-atherogenic properties of wild rice in low-density lipoprotein receptor knockout (LDL-r-KO) mice. The present study aimed to discover the mechanism of action for such effects. MATERIALS: Fecal and plasma samples from the wild rice treated and control mice were used. Fecal bacterial population was estimated while using 16S rDNA technology. The plasma samples were used to estimate the levels of 35 inflammatory markers and metabolomics, while using Meso Scale multiplex assay and liquid chromatography-mass spectrometry (LC-MS/MS) techniques. RESULTS: ., were found in higher quantities in the feces of wild rice fed mice as compared to the controls. Cytokine profiles were significantly different between the plasma of treated and control mice. Among them, an increase in the level of IL-10 and erythropoietin (EPO) could explain the anti-atherogenic properties of wild rice. Among many metabolites tested in plasma of these animals, surprisingly, we found an approximately 60% increase in the levels of glucose in the wild rice fed mice as compared to that in the control mice. CONCLUSION: Additional studies warrant further investigation of the interplay among gut microbiome, inflammatory status, and macronutrient metabolism.
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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 | 0.000 | 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 | 0.001 |
| 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".