Melatonin improves the effect of cryopreservation on human oocytes by suppressing oxidative stress and maintaining the permeability of the oolemma
Bibliographic record
Abstract
Abstract Cryopreservation causes cryoinjury to oocytes and impairs their developmental competence. Melatonin (MLT) can improve the effect of cryopreservation in animal oocytes. However, no such studies on human oocytes have been reported. In this study, collected in vitro‐matured human oocytes were randomly divided into the following groups: fresh group, MLT‐treated cryopreservation (MC) group, and no‐MLT‐treated cryopreservation (NC) group. After vitrification and warming, viable oocytes from these three groups were assessed for their mitochondrial function, ultrastructure, permeability of oolemma, early apoptosis, developmental competence, and cryotolerance‐related gene expression. First, fluorescence staining results revealed that oocytes from the 10−9 M subgroup showed the lowest intracellular reactive oxygen species and Ca2+levels and highest mitochondrial membrane potential among the MC subgroups (10−11, 10−9, 10−7, and 10−5 M). In subsequent experiments, oocytes from the 10−9 M‐MC group were observed to maintain the normal ultrastructural features and the permeability of the oolemma. Compared with those of the oocytes in the NC group, the early apoptosis rate significantly decreased (P < .01), whereas both the high‐quality cleavage embryo and blastocyst rates significantly increased (bothP < .05) in the oocytes of the 10−9 M‐MC group. Finally, single‐cell RNA sequencing and immunofluorescence results revealed that aquaporin (AQP)1/2/11gene expression and AQP1 protein expression were upregulated in the MC group. Therefore, these results suggest that MLT can improve the effect of cryopreservation on human oocytes by suppressing oxidative stress and maintaining the permeability of the oolemma.
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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".