Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts. As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed. To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible. The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans. The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.

Pseudoepitheliomatous hyperplasia (PEH) is a rare histopathological reaction. Cases of PEH have been infrequently reported, and it’s even rare to appear as a postsurgical complication. This case report describes the development of multiple masses and purulent discharge around an abdominal scar following surgery for hepatocellular carcinoma, which morphologically resembles squamous cell carcinoma (SCC), though pathology revealed no signs of malignancy. The underlying mechanism of PEH has not been fully characterized. This case report may alert clinicians and pathologists to rare diseases and update the list of postoperative complications.

Melanoma remains highly lethal with frequent resistance to current therapies. Here we identify a marine-derived cyclopentapeptide, turnagainolide B, as a potent anti-melanoma agent that selectively kills B16-F10 melanoma cells (IC₅₀ = 50 μM) with low toxicity to normal skin cells. Using bioassay-guided isolation, we also obtained a new analogue, turnagainolide H, and elucidated their structures and biosynthetic pathways. Mechanistically, turnagainolide B induces a previously undescribed “dual-hit” autophagic signature: it simultaneously promotes autophagy initiation (via PI3K/mTOR suppression, evidenced by ATG5 and LC3B-II upregulation) and blocks autophagic degradation (evidenced by p62 accumulation). Co-treatment with chloroquine partially rescued cell viability and decreased LC3B levels, confirming that cell death depends on active autophagic flux disruption. Transcriptomic analysis, together with AI target prediction and docking, identified PI3K as a potential direct target, with downregulation of PI3K, mTOR, and BNIP3 supporting an imbalanced autophagic state. In a syngeneic mouse melanoma model, turnagainolide B significantly suppressed tumor growth, reduced melanin content and Ki67 expression, and enhanced CD8⁺ T cell infiltration. Collectively, this work expands the chemical diversity of the turnagainolide family, uncovers a unique “dual-hit” autophagic mechanism, and establishes turnagainolide B as a promising lead for melanoma therapy.