Peptide Hydrogels and Nanostructures Controlling Biological Machinery.

Peptides are versatile building blocks for the fabrication of various nanostructures that result in the formation of hydrogels and nanoparticles. Precise chemical functionalization promotes discrete structure formation, causing controlled bioactivity and physical properties for functional materials development. The conjugation of small molecules on amino acid side chains determines their intermolecular interactions in addition to their intrinsic peptide characteristics. Molecular information affects the peptide structure, formation, and activity. In this Perspective, peptide building blocks, nanostructure formation mechanisms, and the properties of these peptide materials are discussed with the results of recent publications. Bioinstructive and stimuli-responsive peptide materials have immense impacts on the nanomedicine field including drug delivery, cellular engineering, regenerative medicine, and biomedicine.

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.