Influence of Northern Wild Rice on Gut Dysbiosis and Short Chain Fatty Acids: Correlation with Metabolic and Inflammatory Markers in Mice on High Fat Diet
Bibliographic record
Abstract
Wild rice (WLD) attenuated hyperglycemia, hyperlipidemia and chronic inflammation in mice receiving a high-fat diet (HFD) versus white rice (WHR), but the underlying mechanism is not well understood. We examined the influence of HFD + WLD on gut microbiota, short chain fatty acids (SCFAs) and the correlation with metabolic or inflammatory markers in mice versus HFD + WHR. C57BL/6J mice received HFD + 26 g weight (wt) % WHR or WLD or 13 g wt% WHR + 13 g wt% WLD (WTWD) for 12 weeks. Plasma levels of glucose, cholesterol and triglycerides, insulin resistance and inflammatory markers after overnight fasting were lower, and the abundances of fecal Lactobacillus gasseri and propionic acid were higher in HFD + WLD-fed mice than in HFD + WHR-fed mice. The anti-inflammatory effects of HFD + WTWD were weaker than HFD + WLD but were greater than those in HFD + WHR-fed mice. Abundances of fecal Lactobacillus gasseri and propionic acid in mice receiving HFD + WLD were higher than those in mice fed with HFD + WHR. The abundances of fecal L. gasseri and propionic acid negatively correlated with metabolic and inflammatory markers. The findings of the present study suggest that WLD attenuated metabolic and inflammatory disorders in mice on HFD. Interactions between WLD components and gut microbiota may upregulate fecal SCFAs, and the latter may be attributed to the benefits of WLD on metabolism and inflammation in mice on HFD.
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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 | 0.001 |
| 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".