We report here the self-assembly of macroscopic sacs and membranes at the interface between two aqueous solutions, one containing a megadalton polymer and the other, small self-assembling molecules bearing opposite charge. The resulting structures have a highly ordered architecture in which nanofiber bundles align and reorient by nearly 90° as the membrane grows. The formation of a diffusion barrier upon contact between the two liquids prevents their chaotic mixing. We hypothesize that growth of the membrane is then driven by a dynamic synergy between osmotic pressure of ions and static self-assembly. These robust, self-sealing macroscopic structures offer opportunities in many areas, including the formation of privileged environments for cells, immune barriers, new biological assays, and self-assembly of ordered thick membranes for diverse applications.

X-rays to Order: Self-assembly of molecules is often irreversible. Cui et al. (p. 555 , published online 17 December; see the Perspective by Safinya and Li ) examined the ordering of a short peptide sequence (Ala ₆ Glu ₃ ) terminated with an alkyl chain. Aqueous solutions of this molecule could form hexagonally ordered filaments, but more dilute solutions were disordered. However, prolonged x-ray exposure caused these arrays to become ordered. These arrays were stable for several hours but eventually returned to a disordered state; the addition of salts slowed the ordering processes. It is possible that during the ordering process, X-ray–induced charging affected the repulsive forces that balance tension within the filament.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R² of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

Small-molecule self-assembly has proven to be a rich field for the controlled synthesis of supramolecular objects with the size scale of polymers and interesting properties. Using several recent examples from our laboratory, we discuss the development of chemical structure codes for supramolecular self-assembly objects with defined shapes. The resulting materials formed by these objects are promising for electronic functions and biological functions for regenerative medicine.