Nanowarming improves survival of vitrified ovarian tissue and follicular development in a sheep model
Bibliographic record
Abstract
Tissue cryopreservation has allowed long term banking of biomaterials in medicine. Ovarian tissue cryopreservation in particular helps patients by extending their fertility window. However, protection against tissue injury during the thawing process has proven to be challenging. This is mainly due to the heterogenous and slow distribution of the thermal energy across the vitrified tissue during a conventional warming process. Nanowarming is a technique that utilizes hyperthermia of magnetic nanoparticles to accelerate this process. Herein, hyperthermia of synthesized PEGylated silica-coated iron oxide nanoparticles was used to deter the injury of cryopreserved ovarian tissue in a sheep model. When compared to the conventional technique, our findings suggest that follicular development and gene expression in tissues warmed by the proposed technique have been improved. In addition, Nanowarming prevented cellular apoptosis and oxidative stress. We therefore conclude that Nanowarming is a potential complementary candidate to increase efficiency in the ovarian cryopreservation field.
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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".