Comparative effects of a calcium chelator (BAPTA-AM) and melatonin on cryopreservation-induced oxidative stress and damage in ovarian tissue
Bibliographic record
Abstract
Abstract Oncology treatments cause infertility, and ovarian tissue cryopreservation and transplantation (OTCT) is the only option for fertility preservation in prepubertal girls with cancer. However, OTCT is associated with massive follicle loss. Here, we aimed to determine the effect of supplementation of slow freezing and vitrification media with BAPTA-AM and melatonin alone and in combination on ovarian tissue viability, reactive oxygen species (ROS) levels, total antioxidant capacity (TAC), and follicular morphology and viability. Our results indicated that BAPTA-AM and melatonin can significantly improve ovarian tissue viability and the TAC/ROS ratio and reduce ROS generation in frozen-thawed ovarian tissues in slow freezing and vitrification procedures. BAPTA-AM was also found to be less effective on TAC compared to melatonin in vitrified ovarian tissue. While supplementation of slow freezing and vitrification media with BAPTA-AM and/or melatonin could increase the percentage of morphologically intact follicles in cryopreserved ovarian tissues, the differences were not significant. In conclusion, supplementation of cryopreservation media with BAPTA-AM or melatonin improved the outcome of ovarian tissue cryopreservation in both vitrification and slow freezing methods. Our data provide some insight into the importance of modulating redox balance and intracellular Ca2+ levels during ovarian tissue cryopreservation to optimize the current cryopreservation methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".