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Genetic mosaicism for somatic mutations of oncogenes is common in genodermatoses, which can be complicated with extra-cutaneous abnormalities. Here we describe an infant with a congenital anaplastic astrocytoma, a linear syringocystadenoma papilliferum, and ocular abnormalities. The BRAF c.1799T>A p.V600E mutation was detected in both the brain and skin tumor cells but not in the blood or normal skin cells, suggesting somatic mosaicsism for the mutation. Clinically, the brain tumor gradually became life threatening without any response to conventional chemotherapies including carboplatin, etoposide, and temozolomide. Vemurafenib, a BRAF p.V600E inhibitor, was administered daily after the detection of the BRAF mutation. This single-agent therapy was dramatically effective against the anaplastic astrocytoma; the tumor regressed, the cerebrospinal fluid cell count and protein levels decreased to normal levels, and hydrocephalus resolved. Moreover, other lesions including a corneal cyst also responded to vemurafenib. The brain tumor continued shrinking after 6 months of treatment. We present a genodermatosis syndrome associated with BRAF c.1799T>A p.V600E mosaicism. This syndrome may represent a new entity in the mosaic RASopathies, partly overlapping with Schimmelpenning-Feuerstein-Mims syndrome, which is driven by mosaicism of HRAS and/or KRAS activating mutations. Screening for BRAF c.1799T>A p.V600E is especially useful for those with malignant tumors, because it is one of the most-druggable targets.

Recent progress of deep convolutional neural networks (CNNs) proposed by J. Hinton in 2012 succeeded in classifying general images and opened the possibility for automated diagnosis of medical images. However, the application of CNNs to histopathology image classification is, a challenging task. Since the pixel size of virtual slide images, which microscopically scanned a whole histopathology slide, are comprised of 40 to 100 mega pixels and too large to input into usual CNN. To overcome the issue of large pixel files, we developed a novel method, which consists of two CNN models. First, a whole histopathology slide image is dissected into micro images containing one cell per image. Then, the first CNN classifies dissected images into cell and structure types in four categories of atypical cells, epidermis, dermis, and subcutaneous tissue. Based on the result of the first CNN, heatmaps of each cell or structure class are constructed. Then, the second CNN model diagnosis based on the heatmaps, like as Dr. Bernard Ackermans histopathology illustration. We used 60,000 divided micro images of one malignant melanoma virtual slide image as a dataset for the first CNN deep learning. We convert 121 slides of malignant melanoma and 21 slides of nevus pigmentosus to heatmaps and inputted them to the second CNN. Our model showed 73 % of F-score, while a naïve CNN approach that directly input whole histopathology slide images into single CNN achieved 57 % of F-score. We also examined if our method can diagnose other skin tumors, such as basal cell carcinoma, squamous cell carcinoma, seborrheic keratosis, epidermal cyst, Bowens disease, extramammary Pagets disease, and mycosis fungoides. Our result indicated that dermatopathology diagnosis by artificial intelligence requires strategical approaches to reach the levels of experienced dermatopathologists.

Asphalt, also known as bitumen, is a viscous liquid or a semi-solid form of petroleum. In cases of hot liquid asphalt splash, asphalt broadly adheres to the skin surface and is hard to remove from skin. Because accidental burns from hot liquid asphalt splash rarely occur, there is no consensus about initial approaches to remove adherent asphalt from skin. We reviewed articles relating to asphalt burns and summarized methods to remove adherent asphalt from skin, including our present case in which we successfully removed adherent asphalt by edible butter and vegetable oil. We summarized information of 127 cases and classified agents used to remove asphalt in four categories: (i) medicines; (ii) health-care products; (iii) foods; and (iv) solvents. Before the 1990s, antimicrobial topical medicines were mainly reported to treat asphalt burns but it took half a day or more to remove asphalt. Mineral oils and edible oils such as butter and vegetable oil are easily available in grocery stores and could emulsify to remove asphalt in a few hours. From the review of articles and our experience, edible oils are useful agents for the first approach to remove asphalt from the point of view of efficacy, safety, availability and expense.