The transforming growth factor beta superfamily ligand activin A controls juvenile testis growth by stimulating Sertoli cell proliferation. Testicular levels are highest in the first postnatal week, when Sertoli cells are proliferating and spermatogonial stem cells first form. Levels decrease sharply as Sertoli cell proliferation ceases and spermatogenic differentiation begins. We hypothesized that changing activin levels also affect germ cell maturation. We detected an acute and developmentally regulated impact of activin on Kit mRNA in cocultures of Sertoli cells and germ cells from Day 8, but not Day 4, mice. Both stereological and flow cytometry analyses identified an elevated spermatogonium:Sertoli cell ratio in Day 7 testes from Inhba(BK/BK) mice, which have decreased bioactive activin, and the germ cell markers Sycp3, Dazl, and Ccnd3 were significantly elevated in Inhba(BK/BK) mice. The flow cytometry measurements demonstrated that surface KIT protein is significantly higher in Day 7 Inhba(BK/BK) germ cells than in wild-type littermates. By Day 14, the germ cell:Sertoli cell ratio did not differ between genotypes, but the transition of type A spermatogonia into spermatocytes was altered in Inhba(BK/BK) testes. We conclude that regulated activin signaling not only controls Sertoli cell proliferation, as previously described, but also influences the in vivo progression of germ cell maturation in the juvenile testis at the onset of spermatogenesis.

The use of Artificial Intelligence (AI) in teaching has mainly focused on providing students with immediate, and more, feedback while reducing teachers’ workload. In this paper, we propose a shift in thinking about the role AI can play in developing students’ feedback literacy. Through the reflection of a University wide T&L pilot project ran with an AI tool for automated feedback, we frame a discourse on the use of AI for teaching and learning to promote and further develop students’ feedback literacy capabilities, including the dispositions required for a meaningful partnership with their teachers in relation to feedback. This paper reports the interactions of higher education students with an AI automated feedback tool and discusses its affordances in relation to the development of students’ feedback literacy capabilities. Implications for further refinement of this tool are also presented.

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