El Apéndice, referido a Cuenca, de los «Anales de Garci Sánchez, jurado de Sevilla».

Sin resume

Self-Assembling Hydrogels Based on a Complementary Host-Guest Peptide Amphiphile Pair

Supramolecular polymer-based biomaterials play a significant role in current biomedical research. In particular, peptide amphiphiles (PAs) represent a promising material platform for biomedical applications given their modular assembly, tunability, and capacity to render materials with structural and molecular precision. However, the possibility to provide dynamic cues within PA-based materials would increase the capacity to modulate their mechanical and physical properties and, consequently, enhance their functionality and broader use. In this study, we report on the synthesis of a cationic PA pair bearing complementary adamantane and β-cyclodextrin host–guest cues and their capacity to be further incorporated into self-assembled nanostructures. We demonstrate the possibility of these recognition motifs to selectively bind, enabling noncovalent cross-linking between PA nanofibers and endowing the resulting supramolecular hydrogels with enhanced mechanical properties, including stiffness and resistance to degradation, while retaining in vitro biocompatibility. The incorporation of the host–guest PA pairs in the resulting hydrogels allowed not only for macroscopic mechanical control from the molecular scale, but also for the possibility to engineer further spatiotemporal dynamic properties, opening opportunities for broader potential applications of PA-based materials

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation

Background and objectives: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. Methods: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. Results: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. Conclusions: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour