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The objective of this study was to explore the image classification and case characteristics of pigmented nevus (PN) diagnosed by dermoscopy under deep learning. 268 patients were included as the research objects and they were randomly divided into observation group (n = 134) and control group (n = 134). Image recognition algorithm was used for feature extraction, segmentation, and classification of dermoscopic images, and the image recognition and classification algorithm were studied as the performance and accuracy of fusion classifier were compared. The results showed that the classifier was optimized, and the linear kernel accuracy was 85.82%. The PN studied mainly included mixed nevus, junctional nevus, intradermal nevus, and acral nevus. The sensitivity under collaborative training was higher than that under feature training and fusion feature training, and the differences among three trainings were significant (P < 0.05). The sensitivity of the observation group was 88.65%, and the specificity was 90.26%, while the sensitivity and the specificity of the control group were 85.65% and 84.03%, respectively; there were significant differences between the two groups (P < 0.05). In conclusion, dermoscopy under deep learning could be applied as a diagnostic way of PN, which helped improve the accuracy of diagnosis. The dermoscopic manifestations of PN showed a certain corresponding relationship with the type of cases and could provide auxiliary diagnosis in clinical practice. It could be applied clinically. [ABSTRACT FROM AUTHOR]

Therapeutic vaccines that arouse the cytotoxic T cell immune response to reject infected cells have been investigated extensively for treating disease. Due to the large amounts of resident antigen-presenting cells (APCs) and T cells in lymph nodes, great efforts have been made to explore the strategy of targeting lymph nodes directly with nanovaccines to activate T cells. However, these nanovaccines still have several problems, such as a low loading efficiency and compromised activity of antigens and adjuvants derived from traditional complicated preparation. There are also safety concerns about materials synthesized without FDA approval. Herein, we construct an assembled nanoparticle composed of an antigen (ovalbumin, OVA) and adjuvant (CpG) to ensure its safety and high loading efficiency. The activity of both components was well preserved due to the mild self-assembly process. The small size, narrow distribution, negative charge, and good stability of the nanoparticle endow these nanovaccines with superior capacity for lymph node targeting. Correspondingly, the accumulation at lymph nodes can be improved by 10-fold. Subsequently, due to the sufficient APC internalization and maturation in lymph nodes, ~60% of T cells are stimulated to proliferate and over 70% of target cells are specifically killed. Based on the effective and quick cellular immune response, the assembled nanoparticles exhibit great potential as therapeutic vaccines. [ABSTRACT FROM AUTHOR]