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Autopsies were conducted according to published US Centers for Disease Control and Prevention guidelines.3 Six testes samples were tested by reverse transcription-polymerase chain reaction for 3 regions of the COVID-19 virus gene, ORFlabb, N gene, and E gene. In this analysis, evidence of acute testicular injury is seen and is particularly related to oxidative stress, which has previously been reported in animal models in association with type 2 diabetes mellitus.4 We compared our findings to a control group of 7 testes obtained during autopsy from patients who had similar comorbidities and age distribution but were negative for COVID-19. The observed testicular changes in our control group were mainly related to chronic processes (decreased spermatogenesis, reduced Leydig cells, and hyalinization of seminiferous tubules), whereas the changes in COVID-19-positive patients were acute (sloughing of spermatocytes, elongation of spermatids, and swelling of Sertoli cells).

Dermatopathology has rapidly advanced due to the implementation of deep learning models and artificial intelligence (AI). From convolutional neural networks (CNNs) to transformer-based foundation models, these systems are now capable of accurate whole-slide analysis and multimodal integration. This review synthesizes the most recent advents of deep-learning architecture and synthesizes its evolution from first-generation CNNs to hybrid CNN-transformer systems to large-scale foundational models such as Paige’s PanDerm AI and Virchow. Herein, we examine performance benchmarks from real-world deployments of major dermatopathology deep learning models (DermAI, PathAssist Derm), as well as emerging next-generation models still under research and development. We assess barriers to clinical workflow adoption such as dataset bias, AI interpretability, and government regulation. Further, we discuss potential future research directions and emphasize the need for diverse, prospectively curated datasets, explainability frameworks for trust in AI, and rigorous compliance to Good Machine-Learning-Practice (GMLP) to achieve safe and scalable deep learning dermatopathology models that can fully integrate into clinical workflows.

I'm writing in response to John Adams' Feb. 15 \"Fishing Line\" column (\"Reason to get little mad on the Mad\"). The title would make one think this is a fishing report. But, as in the past, Adams uses this column to voice his opinions on fishermen and their methods of fishing. Adams starts this article on a negative note, about obvious snagging on the Mad River in muddy water and the lack of game wardens' involvement, then goes on to report about two fly fishermen who fished the muddy Mad and \"caught\" fish over 20 pounds on flies.Anyone who has ever fished knows when fish are stacked up like they are in the lower Mad, a fly line dragged over a fish is the easiest way to \"snag\" a fish. Fish can't see a tiny fly in muddy water.

I was shocked to read in your paper two negative articles in two days concerning the movie \"Priest.\" I had seen the movie the day before and was moved to laughter and tears as well as being informed, moved and entertained by its message and content.