Restored Agricultural Wetlands in central Iowa: Habitat Quality and Amphibian Response

Amphibians are declining throughout the United States and worldwide due, partly, to habitat loss. Conservation practices on the landscape restore wetlands to denitrify tile drainage effluent and restore ecosystem services. Understanding how water quality, hydroperiod, predation, and disease affect amphibians in restored wetlands is central to maintaining healthy amphibian populations in the region. We examined the quality of amphibian habitat in restored wetlands relative to reference wetlands by comparing species richness, developmental stress, and adult leopard frog (Lithobates pipiens) survival probabilities to a suite of environmental metrics. Although measured habitat variables differed between restored and reference wetlands, differences appeared to have sub-lethal rather than lethal effects on resident amphibian populations. There were few differences in amphibian species richness and no difference in estimated survival probabilities between wetland types. Restored wetlands had more nitrate and alkaline pH, longer hydroperiods, and were deeper, whereas reference wetlands had more amphibian chytrid fungus zoospores in water samples and resident amphibians exhibited increased developmental stress. Restored and reference wetlands are both important components of the landscape in central Iowa and maintaining a complex of fish-free wetlands with a variety of hydroperiods will likely contribute to the persistence of amphibians in this landscape

oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer − a systematic review and expert consensus

Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity, and digital pathology (DP) combined with artificial intelligence (AI) tools (DP-AI) may offer a solution to this challenge. The aim of this work is to summarize the role of DP-AI for PCa patients treated with radiotherapy (RT), and to point out future areas of research. We conducted (1) a systematic review on the evidence of DP-AI for patients treated with RT and (2) a survey of experts using a modified Delphi method, addressing the current role of DP-AI in clinical and research practice to identify relevant fields of future development. Eleven studies investigated DP-AI in PCa RT, with most using the multimodal AI (MMAI) classifier and four ongoing studies are currently prospectively testing the DP-AI performance. DP-AI showed strong prognostic and predictive performance for endpoints like distant metastasis free survival and overall survival, outperforming traditional risk models and assisting treatment decisions such as androgen deprivation therapy (ADT) duration. Fifty-one and 35 experts responded to round 1 and round 2 of the survey respectively. Questions with ≥75 % agreement were considered relevant and included in the qualitative analysis. Survey results confirmed growing adoption of DP scanners, although regional differences in re-imbursement mechanisms and availability persist, with experts endorsing DP-AI's potential across localized, postoperative, and metastatic settings, though further prospective validation is needed. DP-AI tools show strong prognostic and predictive potential in various PCa by guiding patient stratification and optimising ADT duration in primary RT. Prospective studies and validation in cohorts using modern diagnostic and treatment methods are needed before broad clinical adoption