Rice protein suppresses 4-hydroxy-2-nonenal-induced inflammation owing to methionine availability
Bibliographic record
Abstract
4-Hydroxy-2-nonenal (HNE) is one of the most important products of lipid peroxidation, which induces inflammation. To investigate the effects of rice protein (RP) on suppressing HNE-induced inflammation and the role of methionine in regulating the anti-inflammatory function of RP, Wistar rats (male, weighing 180–200 g) were either ad libitum fed a pellet diet with oral administration of methionine or ad libitum fed RP for 2 weeks. RP and methionine significantly reduced HNE levels and effectively suppressed the expressions of cyclooxygenase-2, tumor necrosis factor alpha, interleukin-1β ( IL-1β) and IL-6, and inducible nitric oxide synthase. The anti-inflammatory action of RP was evident from the upregulation of IL-10 and glutathione S-transferase ( GST), which played a role in the detoxification of HNE. The results show that the molecular mechanism responsible for the anti-inflammatory function of RP is the inhibition of nuclear factor kappa B (NF-κB) activation by the downregulation of protein kinase B/phosphoinositide 3 kinase. Further, this study demonstrates that Met availability contributes to the suppression of HNE-induced inflammation through upregulating IL-10 and GST in rats fed RP. Novelty: RP suppresses HNE-induced inflammation. Methionine plays a role in upregulating IL-10 and GST. Methionine availability regulates the inhibition of NF-κB by RP.
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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) | 0.000 | 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".