Immature Oocyte for Fertility Preservation
Bibliographic record
Abstract
obtained in IVF cycles has improved after implementing the vitrification method and many successful results have been reported. Now, this procedure can be successfully offered to fertility preservation programs for patients who are in danger of losing their ovarian function due to medical or social reasons, and to oocyte donation programs. This vitrification technique has also been applied to cryopreserve oocytes obtained from IVM program. Some advantages of oocytes vitrification related with IVM are: (1) eliminating costly drugs and frequent monitoring; (2) completing treatment within 2 to 10 days (3) avoiding the use of hormones in cancer patients with hormone-sensitive tumors; and (4) retrieving oocytes at any point in menstrual cycle, even in the luteal phase. In addition, immature oocytes can also be collected from extracorporeal ovarian biopsy specimens or ovaries during caesarian section. Theoretically, there are two possible approaches for preserving immature oocytes: oocyte cryopreservation at the mature stage (after IVM) and oocyte cryopreservation at the Germinal Vesicle (GV)-stage (before IVM). Both vitrification of immature oocyte before/after IVM is not currently satisfactory. Nevertheless, many IVF centers worldwide are doing IVM oocyte cryopreservation as one of the options to preserve fertility for female cancer. Therefore, more studies are urgently required to improve IVM- and vitrification method to successfully preserve oocytes collected from cancer patients. In this review, present oocyte maturation mechanisms and recent progress of human IVM cycles will be discussed first, followed by some studies of the vitrification of human IVM oocyte.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".