Bioactive Compounds Constituent and Anti-Inflammatory Activity of Natural Rice Bran Oil Produced from Colored and Non-Pigmented Rice in Northern Thailand
Bibliographic record
Abstract
The aims of the study were to measure and compare the content of the bioactive compounds in natural rice bran oils (NRBOs) and investigate for anti-inflammatory activity through inhibition effect on nitric oxide (NO) and inducible nitric oxide synthase (iNOS) in RAW264.7 mouse macrophage cells.NRBOs were prepared from colored and non-pigmented rice in northern Thailand using the cold-press technique. The bioactive compound constituents in NRBOs, including tocotrienols, tocopherols, and γ-oryzanol were analyzed by reversed-phase HPLC. Then, anti-inflammatory activity was investigated through an inhibition effect on NO and iNOS production induced by combined lipopolysaccharide (LPS)-interferon-γ (IFN-γ) in RAW264.7 mouse macrophage cells. The results demonstrated that NRBOs prepared from purple rice, red rice and non-pigmented rice consist of δ, γ, and α-tocotrienol, δ, β, γ, and α-tocopherol, and γ-oryzanol. γ-Oryzanol, γ-tocotrienol, and γ-tocopherol were the major bioactive compounds in NRBOs. NRBOs prepared from purple rice bran exhibited higher concentrations of the bioactive compounds than red rice bran and non-pigmented rice bran, respectively. Khaoʹ′ GamLeum-Phua (KGLP) exhibited the highest amount of δ, γ and α-tocotrienol, δ, γ, β and α-tocopherol, and γ-oryzanol. Interestingly, all NRBOs inhibited NO and iNOS production by LPS/IFN-γ-stimulated RAW264.7 cells. Additionally, NRBO prepared from KGLP exhibited the highest inhibitory activity on NO and iNOS production. There may a potential use for pigmented NRBOs especially cultivated in mountainous areas which containing high amounts of tocotrienols, tocopherols, and γ-oryzanol, as a natural anti-inflammatory active ingredient in nutraceutical and cosmeceutical products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".