Quantification of follicles in human ovarian tissue using image processing software and trained artificial intelligence†.

Cancer survival rates in prepubertal girls and young women have risen in recent decades due to increasingly efficient treatments. However, many such treatments are gonadotoxic, causing premature ovarian insufficiency, loss of fertility, and ovarian endocrine function. Implantation of donor ovarian tissue encapsulated in immune-isolating capsules is a promising method to restore physiological endocrine function without immunosuppression or risk of reintroducing cancer cells harbored by the tissue. The success of this approach is largely determined by follicle density in the implanted ovarian tissue, which is analyzed manually from histologic sections and necessitates specialized, time-consuming labor. To address this limitation, we developed a fully automated method to quantify follicle density that does not require additional coding. We first analyzed ovarian tissue from 12 human donors between 16 and 37 years old using semi-automated image processing with manual follicle annotation and then trained artificial intelligence program based on follicle identification and object classification. One operator manually analyzed 102 whole slide images from serial histologic sections. Of those, 77 images were assessed by a second manual operator, followed with an automated method utilizing artificial intelligence. Of the 1181 follicles the control operator counted, the comparison operator counted 1178, and the artificial intelligence counted 927 follicles with 80% of those being correctly identified as follicles. The three-stage artificial intelligence pipeline finished 33% faster than manual annotation. Collectively, this report supports the use of artificial intelligence and automation to select tissue donors and grafts with the greatest follicle density to ensure graft longevity for premature ovarian insufficiency treatment.

Heparin-hyaluronic acid nanofibers for growth factor sequestration in spinal cord repair.

Growth factor (GF) delivery is a common strategy for spinal cord injury repair, however, GF degradation can impede long-term therapies. GF sequestration via heparin is known to protect bioactivity after delivery. We tested two heparin modifications, methacrylated heparin and thiolated heparin, and electrospun these with methacrylated hyaluronic acid (MeHA) to form HepMAHA and HepSHHA nanofibers. For loaded conditions, MeHA, HepMAHA, and HepSHHA fibers were incubated with soluble basic fibroblast growth factor (bFGF) or nerve growth factor (NGF) and rinsed with PBS. Control groups were hydrated in PBS. L929 fibroblast proliferation was analyzed after 24 hr of culture in either growth media or bFGF-supplemented media. Dissociated chick dorsal root ganglia neurites were measured after 3 days of cell culture in serum free media (SFM) or NGF-supplemented SFM (SFM + NGF). In growth media, fibroblast proliferation was significantly increased in loaded HepMAHA (α < .05) compared to other groups. In SFM, loaded HepMAHA had the longest average neurite length compared to all other groups. In SFM + NGF, HepMAHA and HepSHHA had increased neurite lengths compared to MeHA, regardless of loading (α < .01), suggesting active sequestration of soluble NGF. HepMAHA is a promising biomaterial for sequestering released GFs in a spinal cord injury environment and will be combined with GF filled microspheres for future studies.