Selenium-rich maize modulates the expression of prostaglandin genes in lipopolysaccharide-stimulated RAW264.7 macrophages
Bibliographic record
Abstract
Cell signaling is necessary for the organs to co-ordinate with the whole body and it includes response to external stimuli, inflammation, hormonal secretions and other various metabolic functions. In the present study, we have focused on the inflammatory signals modulated by the reactive oxygen and nitrogen species (RONS). Under homeostatic conditions, these species turn on the COX-1-dependent arachidonic acid (AA) pathway towards the release of anti-inflammatory enzymes. However, the excess release of these ions induces negative effects in the form of inflammation by turning on the COX-2-dependent AA pathway to release pro-inflammatory enzymes. In the present study, we observed the shunting of the COX-2-dependent AA pathway towards the release of anti-inflammatory enzymes with the supplementation of organic dietary selenium in the form of seleniferous maize extracts. We observed that 500 nM selenium concentration in Se-maize extracts downregulated the COX-2 and mPGES-1 expressions by 3.8- and 3.2-fold and upregulated the GPx-1 and H-PGDS expressions by 5.0- and 5.4-fold, respectively. To facilitate more availability of Se from the dietary matrices, Se-maize extracts were incubated with rMETase. It was observed that the enzyme-treated cells increased the downregulation of COX-2 and mPGES-1 expressions by 24.8- and 21.0-fold and the upregulation of GPx-1 and H-PGDS expressions by 13.2- and 16.5-fold, respectively.
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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 | 0.000 |
| 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".