Exploiting the bioactive properties of γ-oryzanol from bran of different exotic rice varieties
Bibliographic record
Abstract
The rice industry is one of the most significant food industries since rice is a widely consumed cereal in the world. As a result of this substantial production, the rice industry has a significant amount of side streams, including bran, representing millions of tons of raw materials mainly designated to animal feed. Rice bran is a rich source of γ-oryzanol, a bioactive compound with substantial health benefits. In this perspective, different bran rice samples from distinct germplasm origins (Philippines, Italy and Portugal) were studied for their γ-oryzanol content by HPLC-PDA, cytotoxicity in four human tumour cell lines, hepatotoxicity in a normal cell line and for their antimicrobial effects on different bacterial and fungal strains. The Ballatinao sample presented the strongest activity against all the tumour cell lines, and was also the sample showing the highest amount of γ-oryzanol, suggesting its contribution to the exhibited cytotoxic properties. Regarding the antimicrobial activity, the tested samples were able to inhibit the majority of bacterial and fungal strains, with the Portuguese Ceres sample being the one presenting the highest bacterial inhibition and the Maluit and Dinorado samples, the highest fungal inhibition. Overall, the results show that rice bran extracts may be considered as potential candidates for antimicrobial agents when incorporated into food matrices.
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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 | 0.000 |
| 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".