Delay in oocyte aging in mice by the antioxidant N-acetyl-l-cysteine (NAC)
Bibliographic record
Abstract
BACKGROUND: Ovarian aging is associated with declining numbers and quality of oocytes and follicles. Oxidative stress by reactive oxygen species (ROS) contributes to somatic aging in general, and also has been implicated in reproductive aging. Telomere shortening is also involved in aging, and telomeres are particularly susceptible to ROS-induced damage. Previously, we have shown that antioxidant N-acetyl-L-cysteine (NAC) effectively rescues oocytes and embryos from ROS-induced telomere shortening and apoptosis in vitro. Using mice as models, we tested the hypothesis that reducing oxidative stress by NAC might prevent or delay ovarian aging in vivo. METHODS: Initially, young females were treated with NAC in drinking water for 2 months and the quality of fertilized oocytes and early embryo development were evaluated. Next, young mice 1-1½ months old were treated for 1 year with NAC added in drinking water, and their fertility was analyzed starting at 6 months, as indicated by litter size, oocyte number and quality. The ovaries were also examined for telomere activity and length and the expression of selected genes related to aging and DNA damage. RESULTS: Short-term treatment of mice for 2 months with NAC demonstrated that NAC improved the quality of fertilized oocytes and early embryo development. Mice treated with a long-term low concentration (0.1 mM) of NAC had increased litter sizes at the ages of 7-10 months compared with age-matched controls without NAC treatment. NAC also increased the quality of the oocytes from these older mice. Moreover, the expression of sirtuins was increased, telomerase activity was higher and telomere length was longer in the ovaries of mice treated with NAC compared with those of the control group. CONCLUSIONS: These data suggest that appropriate treatment with the antioxidant NAC postpones the process of oocyte aging in mice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".