By rationally adjusting the weaving modes of point‐star tiles, the curvature inherent in the tiles can be changed, and various DNA nanostructures can be assembled, such as planar wireframe meshes, perforated wireframe tubes, and curved wireframe polyhedra. Based on the weaving and tiling architectures for traditional point‐star tiles with the core arm length at two DNA half‐turns, we improved the weaving modes of our newly reported four‐point‐star tiles with the core arm length at three half‐turns to adjust their curvature and rigidity for assembling 2D arrays of DNA grids and tubes. Following our previous terms and methods to analyze the structural details of E‐tiling tubes, we used the chiral indices (n,m) to describe the most abundant tube of typical assemblies; especially, we applied both one‐locus and/or dual‐locus biotin/streptavidin (SA) labelling strategies to define the configurations of two specific tubes, along with the absolute conformations of their component tiles. Such structural details of the DNA tubes composed of tiles with addressable concave and convex faces and packing directions should help us understand their physio‐chemical and biological properties, and therefore promote their applications in drug delivery, biocatalysis, biomedicine, etc.

Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C -index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.