Regulation of NADPH Oxidase Mediated Oxidative Stress in Macrophages
Bibliographic record
Abstract
Oxidative stress contributes to the pathogenesis of human diseases including atherosclerosis. The NADPH oxidase is a key source of oxidative stress in atherogenesis. Our objectives were to determine the effect of berberine, herbal medicinal compound, on NADPH oxidase‐mediated production in monocyte‐derived macrophage (THP‐1). Cells were pretreated with berberine (10–50umol/L) followed by the addition of lipopolysaccharide (LPS) (10ug/L) to induce activation of NADPH oxidase. Treatment with LPS significantly increased intracellular O 2 − over a 24 h period. Pre‐incubation with berberine resulted in a concentration‐dependent inhibition of O 2 − generation in these cells. Real‐time PCR analysis revealed an increased expression of all NADPH oxidase subunits in LPS‐treated cells. However, pretreatment with berberine (25umol/L) display a significant reduction in gp91 phox mRNA expression. Transfection with gp91 phox siRNA abolished LPS‐induced O 2 − generation in macrophages, confirming the essentiality of this subunit to oxidase activation. Cell viability tests confirmed that berberine, at concentrations sufficient for inhibiting NADPH oxidase‐mediated O 2 − generation in macrophages, did not affect cell viability. In conclusion, berberine can effectively regulate NADPH oxidase activity in stimulated macrophages by selectively inhibiting the expression of the catalytic subunit gp91 phox .
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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".