A stable, good coverage of the corneal tissue by the tear film is essential for protecting the eye. Contact lenses, however, constitute a foreign body that separates the tear film into two thinner layers, which are then more vulnerable toward disruption. This effect is even more pronounced if the contact lenses possess an insufficient surface wettability, which, in addition to friction, is suggested to be linked to discomfort and damage to the ocular surface. In this study, we establish covalent surface coatings with mucin macromolecules to overcome this issue for pure silicone contact lenses. This material class, which outperforms state-of-the-art silicone hydrogels in terms of oxygen permeability, is not yet used for commercial contact lens applications, which is due to its strongly hydrophobic surface characteristics. The applied process stably attaches a transparent mucin layer onto the contact lenses and thereby establishes hydrophilic surfaces that not only prevent lipid adsorption but also interact very well with liquid environments. Most importantly, however, we show that those mucin coatings are indeed able to prevent wear formation on corneal tissue that is subjected to the tribological stress applied by a contact lens. Our results open up great possibilities for a variety of hydrophobic materials that are, to date, not suitable for a contact lens application. Furthermore, the ability of mucin coatings to reduce wear in a tissue/synthetic material contact might be also beneficial for other biomedical applications.

Hydrogels made of crosslinked macromolecules used in regenerative medicine technologies can be designed to affect the fate of surrounding cells and tissues in defined ways. Their function typically depends on the type and number of bioactive moieties such as receptor ligands present in the hydrogel. However, the detail in how such moieties are presented to cells can also be instrumental. In this work, how the crosslinking architecture of a hydrogel can affect its bioactivity is explored. It is shown that bovine submaxillary mucins, a highly glycosylated and immune‐modulating protein, exhibit strikingly different bioactivities whether they are crosslinked through their glycans or their protein domains. Both the susceptibility to enzymatic degradation and macrophage response are affected, while rheological properties and barrier to diffusion are mostly unaffected. The results suggest that crosslinking architecture affects the accessibility of the substrate to proteases and the pattern of sialic acid residues exposed to the macrophages. Thus, modulating the accessibility of binding sites through the choice of the crosslinking strategy appears as a useful parameter to tune the bioactivity of hydrogel‐based systems.

Recent research indicates that the progression of Parkinson's disease can start from neurons of the enteric nervous system, which are in close contact with the gastrointestinal epithelium: α-synuclein molecules can be transferred from these epithelial cells in a prion-like fashion to enteric neurons. Thin mucus layers constitute a defense line against the exposure of noninfected cells to potentially harmful α-synuclein species. We show that despite its mucoadhesive propertiesα-synuclein can translocate across mucin hydrogels, and this process is accompanied by structural rearrangements of the mucin molecules within the gel. Penetration experiments with different α-synuclein variants and synthetic peptides suggest that two binding sites on α-synuclein are required to accomplish this rearrangement of the mucin matrix. Our results support the notion that the translocation of α-synuclein across mucus barriers observed here might be a critical step in the infection of the gastrointestinal epithelium and the development of Parkinson's disease.

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fastevolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by timeconsuming trial and error approaches and our limited understanding of how different material properties depend on each other. Here, we apply machine learning algorithms to classify complex biological materials based on their microtopography. With this approach, the surfaces of different variants of biofilms and plant leaves can not only be distinguished but also correctly classified according to their wettability. Furthermore, an importance ranking provided by one of the algorithms allows us to identify those surface features that are critical for a successful sample classification. Our study exemplifies how machine learning can contribute to the analysis and categorization of complex surfaces, a tool, which can be highly useful for other areas of materials science, such as damage assessment as well as adhesion or friction studies.