Oxidative Stress-related Alteration of the Copy Number of Mitochondrial DNA in Human Leukocytes
Bibliographic record
Abstract
The role of oxidative stress in the regulation of the copy number of mitochondrial DNA (mtDNA) in human leukocytes is unclear. In this study, we investigated the redox factors in plasma that may contribute to the alteration of mtDNA copy number in human leukocytes. A total of 156 healthy subjects of 25-80 years of age who exhibited no significant difference in the distribution of subpopulations of leukocytes in blood were recruited. Small-molecular-weight antioxidants and thiobarbituric acid reactive substances (TBARS) in plasma and 8-hydroxy-2'-deoxyguanosine (8-OHdG) and 4,977bp deletion of mtDNA in leukocytes were determined. The mtDNA copy number in leukocytes was determined by real-time PCR. The results showed that the copy number of mtDNA in leukocytes was changed with age in a biphasic manner that fits in a positively quadratic regression model (P = 0.001). Retinol (P = 0.005), non-protein thiols (P = 0.001) and ferritin (P = 0.004) in plasma and total glutathione in erythrocytes (P = 0.046) were the significant redox factors that correlated with the mtDNA copy number in leukocytes in a positive manner. By contrast, alpha-tocopherol levels in plasma (P = 0.001) and erythrocytes (P = 0.033) were negatively correlated with the mtDNA copy number in leukocytes. Three oxidative indices including the incidence of 4,977 bp deletion of mtDNA (P = 0.016) and 8-OHdG content in leukocytes (P = 0.003) and TBARS in plasma (P = 0.001) were all positively correlated with the copy number of mtDNA in leukocytes. Taken these findings together, we suggest that the copy number of mtDNA in leukocytes is affected by oxidative stress in blood circulation elicited by the alteration of plasma antioxidants/prooxidants and oxidative damage to DNA.
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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 | 0.001 |
| 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".