Folic acid inhibits homocysteine-induced superoxide anion production and nuclear factor kappa B activation in macrophagesThis paper is one of a selection of papers published in this Special Issue, entitled Young Investigator's Forum.
Bibliographic record
Abstract
Folic acid supplementation is a promising approach for patients with cardiovascular diseases associated with hyperhomocysteinemia. We have demonstrated that homocysteine (Hcy) activates nuclear factor-kappaB (NF-kappaB), a transcription factor that plays an important role in inflammatory responses. The aim of the present study was to investigate the effect of folic acid on Hcy-induced NF-kappaB activation in macrophages. Hcy treatment (100 micromol/L) resulted in NF-kappaB activation and increased monocyte chemoattractant protein-1 (MCP-1) expression in THP-1 derived macrophages. Hcy-induced NF-kappaB activation was associated with a significant increase in the intracellular superoxide anion levels. There was a significant increase in phosphorylation and membrane translocation of NADPH oxidase p47phox subunit in Hcy-treated cells. Addition of folic acid (200 ng/mL) to the culture medium abolished NADPH oxidase-dependent superoxide anion generation in macrophages by preventing phosphorylation of p47phox subunit. Consequently, Hcy-induced NF-kappaB activation and MCP-1 expression was inhibited. Such an inhibitory effect of folic acid was independent of its Hcy-lowering ability. Taken together, these results suggest that folic acid treatment can effectively inhibit Hcy-induced oxidative stress and inflammatory responses in macrophages. This may represent one of the mechanisms by which folic acid supplementation exerts a protective effect in cardiovascular disorders.
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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) | 0.002 | 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".