Girls and young women who require ovariectomy or cancer therapy may consider having their own eggs, embryos or ovarian tissue stored (cryopreserved) for their own future use. Ovarian tissue is simple to collect and contains large numbers of germ cells. Transplantation of fresh and frozen-thawed ovarian tissue in healthy sheep and mice has resulted in normal live young. Similar techniques may be effective in the human but it is unclear whether ovarian tissue cryopreservation and grafting is suitable for ovaries from individuals with cancer or infections. If cancer cells were present in an ovary at the time of collection and survived cryopreservation and grafting they could establish cancer in the recipient. We therefore performed ovarian cryopreservation and transplantation trials using a mouse lymphoma model. This established that the lymphoma was transmitted by grafts of both fresh and frozen ovarian tissue. The normal healthy recipient mice died 9-43 days after receiving a small piece (1 mm3) of ovarian tissue from a donor with lymphoma. We conclude that ovarian tissue which is collected, cryopreserved and grafted while it contains cancer cells has the potential to spread the cancer to the graft recipient.

A cell-free culture system was designed for human embryo development to the blastocyst stage by testing a range of culture conditions in a series of protocols. The culture system that was evolved has a brief 1 h exposure to spermatozoa and then culture of the pronucleate zygote for 2 days in IVF-50 medium. Two or three embryos were cultured together in 20 microl microdrops of medium under oil. Embryos were then regrouped and two or three at a similar stage were cultured together in 50 microl microdrops of Gardner's G2 medium under oil from days 3 to 5. Embryos were transferred to fresh G2 medium on day 5 and cultured for a further 1 or 2 days (day 6 or 7). No serum was used in any of the cultures. The embryo transfer medium and G2 medium were supplemented with human serum albumin. The zonae of all blastocysts to be transferred to patients were completely removed enzymatically. Using this protocol, 52% of zygotes developed to blastocysts and 34 out of 35 patients treated received 82 blastocysts and 11 morulae on day 5 or 6. Twenty-one fetal sacs with positive heartbeats (23% implantation rate) were detected in 13 ongoing pregnancies (38% pregnancy rate/transfer or 37%/patient treated). We anticipate that further improvements in embryo development and the selection of viable embryos can be achieved using this simple and effective culture system.

No abstract

No abstract

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.