Genistein inhibits the development of atherosclerosis via inhibiting NF-κB and VCAM-1 expression in LDLR knockout mice
Bibliographic record
Abstract
Diet can be an important factor that influences risks for cardiovascular disease. Genistein (4',5,7-trihydroxyisoflavone), rich in soy, is one candidate that may benefit the cardiovascular system. Here, we explored the effect of genistein in atherosclerosis (AS) development in an in vivo mouse model. Low-density lipoprotein receptor (LDLR) knockout mice were allocated to control, model, and genistein groups. Our results showed that genistein significantly reduced the formation and development of atherosclerotic plaques ((4.68 +/- 1.18) x106 versus (6.65 +/- 1.51) x106 microm2, p < 0.05). In the genistein group, compared with the model group, total antioxidant capacity (TAC) level was 85.5 +/- 15.6 versus 203.4 +/- 32.6 mmol/L (p < 0.01); malondialdehyde (MDA) level was 3.79 +/- 0.28 versus 3.06 +/- 0.31 mmol/L (p < 0.01), and superoxide dismutase (SOD) activity was 86.1 +/- 6.1 versus 139.1 +/- 25.1 U/mL (p < 0.01). Therefore, genistein was able to enhance serum antioxidative ability in our mouse model. Genistein had no influence, however, on serum cholesterol and lipid profiles. Genistein also markedly downregulated the expression of nuclear factor (NF)-kappaB and vascular cell adhesion molecule (VCAM)-1 in aortas of mice (p < 0.05). These observations suggest that genistein may inhibit AS in LDLR-/- mice via enhancing serum antioxidation and downregulating NF-kappaB and VCAM-1 expression in the aorta.
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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) | 0.000 | 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".