Triggering shape-memory functionality under clinical hyperthermia temperatures could enable the control and actuation of shape-memory systems in clinical practice. For this purpose, we developed light-inducible shape-memory microparticles composed of a poly(d,l-lactic acid) (PDLLA) matrix encapsulating gold nanoparticles (Au@PDLLA hybrid microparticles). This shape-memory polymeric system for the first time demonstrates the capability of maintaining an anisotropic shape at body temperature with triggered shape-memory effect back to a spherical shape at a narrow temperature range above body temperature with a proper shape recovery speed (37 < T < 45 °C). We applied a modified film-stretching processing method with carefully controlled stretching temperature to enable shape memory and anisotropy in these micron-sized particles. Accordingly, we achieved purely entanglement-based shape-memory response without chemical cross-links in the miniaturized shape-memory system. Furthermore, these shape-memory microparticles exhibited light-induced spatiotemporal control of their shape recovery using a laser to trigger the photothermal heating of doped gold nanoparticles. This shape-memory system is composed of biocompatible components and exhibits spatiotemporal controllability of its properties, demonstrating a potential for various biomedical applications, such as tuning macrophage phagocytosis as demonstrated in this study.

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Purpose of review Although deep learning represents an exciting platform for the development of risk stratification models, it is challenging to evaluate these models beyond simple statistical measures of success, which do not always provide insight into a model's clinical utility. Here we propose a framework for evaluating deep learning models and discuss a number of interesting applications in light of these rubrics. Recent findings Data scientists and clinicians alike have applied a variety of deep learning techniques to both medical images and structured electronic medical record data. In many cases, these methods have resulted in risk stratification models that have improved discriminatory ability relative to more straightforward methods. Nevertheless, in many instances, it remains unclear how useful the resulting models are to practicing clinicians. Summary To be useful, deep learning models for cardiovascular risk stratification must not only be accurate but they must also provide insight into when they are likely to yield inaccurate results and be explainable in the sense that health care providers can understand why the model arrives at a particular result. These additional criteria help to ensure that the model can be faithfully applied to the demographic for which it is most accurate.