Beneficial Effects of Germinated Brown Rice on Cardiovascular Risk Factors in LDL Receptor Knockout Mice
Bibliographic record
Abstract
Based on accumulating evidence, adequate intake of whole grains is associated with reduced cardiovascular disease CVD risk. Germinated brown rice (GBR) has been used in East Asian countries as an alternative grain. Preliminary studies suggest GBR has potential health benefits, including reducing CVD risk, but the mechanism remains unclear. The hypothesis of the project is that long-term consumption of GBR would reduce atherogenic risk factors in low-density lipoprotein receptor knockout (LDLr-KO) mice. To test the hypothesis, three groups of male LDLr-KO mice were fed with one of the following diets for 24 weeks: (a) commercial mouse chow, used as the control diet; (b) chow was replaced with 60% (w/w) Chinese white rice (CWR); and (c) chow was replaced with 60% (w/w) GBR. All diets were supplemented with 0.06% (w/w) dietary cholesterol to accelerate atherogenesis. Blood samples, hearts, livers and feces were collected and used for biochemical and histological analyses. The results demonstrated that no significant difference was detected in body weights, plasma or fecal lipid profiles and antioxidant enzyme activities among groups. However, GBR consumption significantly decreased atherosclerotic lesion (P = 0.003) in the aortic roots as compared with that in the CWR group, but there was no significant difference as compared with that in the control group (P = 0.4). In addition, GBR significantly decreased monocyte adhesion to the aorta in LDLr-KO mice as compared to that in the CWR group (P=0.0001), but not with the control group. These data suggested that GBR may be beneficial for the prevention of vascular inflammation and atherogenesis in LDLr-KO mice. Additional studies in animal models and humans may further investigate the mechanisms of the beneficial effects of GBR on vascular inflammation and atherogenesis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".