ReviewMedical science monitor : international medical journal of experimental and clinical research2025
Developing Mouse Models for Ovarian Tissue Transplantation and Xenotransplantation: A Review.
Review in Medical science monitor : international medical journal of experimental and clinical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- From subclinical ovarian damage to reproductive outcomes: a translational perspective on PI3K/AKT/mTOR signaling in fertility preservation.Journal of assisted reproduction and genetics · 2026Review
- Cryopreservation of Rodent Ovarian Tissues and Oocytes.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Protective Effect of Glutathione Supplementation On Mouse Ovarian Tissue Vitrification and Autologous Transplantation.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian tissue transplantation (OTT) is the only option for preserving fertility in prepubertal girls and young women who require immediate cancer treatment. Due to ethical constraints and the limited number of clinical cases, human OTT research is challenging. Mouse OTT models serve as valuable preclinical models. This article aims to review the current status of mouse OTT models, including xenotransplantation from humans. We conducted a systematic analysis of original research articles and reviews of mouse OTT models published in PubMed and the China National Knowledge Infrastructure (CNKI). The construction methods included different mouse strains (C57/BL6, Institute of Cancer Research, Naval Medical Research Institute, genetically engineered, and immunodeficient mice), transplantation sites (subcutaneous tissue, sub-renal capsule, back muscle, peritoneum, and ovarian bursa), and transplantation types (xenotransplantation, allogeneic, and autologous transplantation). The evaluation metrics included histological analysis, assessment of neovascularization density, measurement of granulosa cell proliferation activity, and hormonal and estrous cycle monitoring. The choice of metrics should be selected according to the stage after transplantation. To advance the clinical application, mouse OTT models could be improved by developing standardized evaluation criteria and simplified, rapid, noninvasive detection methods to enhance consistency and comparability of research outcomes. Key areas for further research include addressing safety concerns (eg, risk of tumor cell reimplantation), optimizing efficacy evaluations (eg, follicle quality and endocrine function recovery), and improving cost-effectiveness through analysis of mouse strains and transplantation protocols. This review provides valuable insights for future research and clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.