Folic acid supplementation inhibits homocysteine‐induced superoxide anion production and chemokine expression in the kidney
Bibliographic record
Abstract
Hyperhomocysteinemia (HHcy), a condition of elevated blood homocysteine (Hcy) levels, is a metabolic disease. Kidney dysfunction is a common risk factor causing HHcy. Recent studies indicate that HHcy also lead to renal injury by inducing oxidative stress and expression of inflammatory markers. Monocyte chemoattractant protein‐1 (MCP‐1) is a potent chemokine that stimulates monocyte/macrophage accumulation in the kidney. Upregulation of this chemokine is associated with inflammatory injury. Although folic acid supplementation can reduce blood Hcy levels, its effect on the kidney is not clear. The aim of this study was to investigate the effect of folic acid on Hcy‐induced superoxide generation and chemokine expression in the kidney. HHcy was induced in Sprague Dawley rats fed a high‐methionine diet. The NADPH oxidase‐mediated superoxide production and MCP‐1 mRNA expression were significantly increased in HHcy rat kidneys. Folic acid supplementation effectively abolished HHcy‐induced oxidative stress by inhibition of NADPH oxidase activation. This, in turn, prevented HHcy‐induced chemokine expression in the kidney. Such a beneficial effect of folic acid is not directly related to Hcy reduction. Our results suggest that folic acid supplementation may offer a renal protective effect by antagonizing oxidative stress‐mediated inflammatory response. (This study was supported by NSERC and CIHR)
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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".