Fabrication of Self-Wrinkling Polymer Films with Tunable Patterns through an Interfacial-Fuming-Induced Surface Instability Process.

Inspired by the superglue fuming method for fingerprint collection, this study developed a novel interfacial-fuming-induced surface instability process to generate wrinkled patterns on polymeric substrates. High-electronegativity groups are introduced on the substrate surface to initiate the polymerization of monomer vapors, such as ethyl cyanoacrylate, which results in the formation of a stiff poly(ethyl cyanoacrylate) capping layer. Moreover, interfacial polymerization resulted in the covalent bonding of the substrate, which led to the volumetric shrinkage of the composite and the accumulation of compressive strain. This process ultimately resulted in the development and stabilization of wrinkled surface morphologies. The authors systematically examined parameters such as the modulus of the epoxy substrate, prestrain, the flow rate of fuming, and operating temperature. The aforementioned technique can be easily applied to architectures with complex outer morphologies and inner surfaces, thereby enabling the construction of surface patterns under ambient conditions without vacuum limitations or precise process control. This study is the first to combine fuming-induced interfacial polymerization with surface instability to create robust wrinkles. The proposed method enables the fabrication of intricate microwrinkled patterns and has considerable potential for use in various practical applications, including microfluidics, optical components, bioinspired adhesive devices, and interfacial engineering.

Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study.

BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. METHODS: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10,338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at five centers. Five radiology residents and five attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. RESULTS: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (p = 0.67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (p < 0.001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs. 0.03 [0.03-0.03]; p < 0.001), but a similar time reduction (reduced median time, 44% [40-47%] vs. 40% [37-44%]; p = 0.92) with BMSS assistance. CONCLUSIONS: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.