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This study analyzed skin biopsies from human monkeypox virus (hMPXV1) cases and found characteristic pustular stage features, including epidermal necrosis, shadow cell appearance, ballooned keratinocytes, and a dense inflammatory infiltrate, expanding histological knowledge crucial for understanding this disease and related Orthopoxvirus infections.

This study explored using large language models to automatically identify monkeypox cases from electronic medical records, finding that models based on DeepSeek features performed better than traditional methods, with logistic regression showing high accuracy in detecting key symptoms like fever and rash.

This study observed that epidermal necrosis and keratinocytic ballooning are consistent histopathologic signs of monkeypox in skin lesions, reinforcing the diagnostic value of histopathologic and immunohistochemical analyses.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.