Spider dragline silk is one of the strongest, most extensible and toughest biological materials known, exceeding the properties of many engineered materials including steel. Silk features a hierarchical architecture where highly organized, densely H-bonded beta-sheet nanocrystals are arranged within a semiamorphous protein matrix consisting of 3₁-helices and beta-turn protein structures. By using a bottom-up molecular-based approach, here we develop the first spider silk mesoscale model, bridging the scales from Angstroms to tens to potentially hundreds of nanometers. We demonstrate that the specific nanoscale combination of a crystalline phase and a semiamorphous matrix is crucial to achieve the unique properties of silks. Our results reveal that the superior mechanical properties of spider silk can be explained solely by structural effects, where the geometric confinement of beta-sheet nanocrystals, combined with highly extensible semiamorphous domains, is the key to reach great strength and great toughness, despite the dominance of mechanically inferior chemical interactions such as H-bonding. Our model directly shows that semiamorphous regions govern the silk behavior at small deformation, unraveling first when silk is being stretched and leading to the large extensibility of the material. Conversely, beta-sheet nanocrystals play a significant role in defining the mechanical behavior of silk at large-deformation. In particular, the ultimate tensile strength of silk is controlled by the strength of beta-sheet nanocrystals, which is directly related to their size, where small beta-sheet nanocrystals are crucial to reach outstanding levels of strength and toughness. Our results and mechanistic insight directly explain recent experimental results, where it was shown that a significant change in the strength and toughness of silk can be achieved solely by tuning the size of beta-sheet nanocrystals. Our findings help to unveil the material design strategy that enables silk to achieve superior material performance despite simple and inferior material constituents. This concept could lead to a new materials design paradigm, where enhanced functionality is not achieved using complex building blocks but rather through the utilization of simple repetitive constitutive elements arranged in hierarchical structures from nano to macro.

Abdominal aortic aneurysm (AAA) disease is a degenerating process whose ultimate event is the rupture of the vessel wall. Rupture occurs when the stresses acting on the wall rise above the strength of the AAA wall tissue. The complex mechanical interaction between blood flow and wall dynamics in a three dimensional custom model of a patient AAA was studied by means of computational coupled fluid-structure interaction analysis. Real 3D AAA geometry is obtained from CT scans image processing. The results provide a quantitative local evaluation of the stresses due to local structural and fluid dynamic conditions. The method accounts for the complex geometry of the aneurysm, the presence of a thrombus and the interaction between solid and fluid. A proven clinical efficacy may promote the method as a tool to determine factual aneurysm risk of rupture and aid the surgeon to refer elective surgery patients.

The catalytic properties of some selected enzymes have long been exploited to carry out efficient and cost-effective bioconversions in a multitude of research and industrial sectors, such as food, health, cosmetics, agriculture, chemistry, energy, and others. Nonetheless, for several applications, naturally occurring enzymes are not considered to be viable options owing to their limited stability in the required working conditions. Over the years, the quest for novel enzymes with actual potential for biotechnological applications has involved various complementary approaches such as mining enzyme variants from organisms living in extreme conditions (extremophiles), mimicking evolution in the laboratory to develop more stable enzyme variants, and more recently, using rational, computer-assisted enzyme engineering strategies. In this review, we provide an overview of the most relevant enzymes that are used for industrial applications and we discuss the strategies that are adopted to enhance enzyme stability and/or activity, along with some of the most relevant achievements. In all living species, many different enzymes catalyze fundamental chemical reactions with high substrate specificity and rate enhancements. Besides specificity, enzymes also possess many other favorable properties, such as, for instance, cost-effectiveness, good stability under mild pH and temperature conditions, generally low toxicity levels, and ease of termination of activity. As efficient natural biocatalysts, enzymes provide great opportunities to carry out important chemical reactions in several research and industrial settings, ranging from food to pharmaceutical, cosmetic, agricultural, and other crucial economic sectors.

The need to optimize the thrombogenic performance of blood recirculating cardiovascular devices, e.g., prosthetic heart valves (PHV) and ventricular assist devices (VAD), is accentuated by the fact that most of them require lifelong anticoagulation therapy that does not eliminate the risk of thromboembolic complications. The formation of thromboemboli in the flow field of these devices is potentiated by contact with foreign surfaces and regional flow phenomena that stimulate blood clotting, especially platelets. With the lack of appropriate methodology, device manufacturers do not specifically optimize for thrombogenic performance. Such optimization can be facilitated by formulating a robust numerical methodology with predictive capabilities of flow-induced platelet activation. In this study, a phenomenological model for platelet cumulative damage, identified by means of genetic algorithms (GAs), was correlated with in vitro experiments conducted in a Hemodynamic Shearing Device (HSD). Platelets were uniformly exposed to flow shear representing the lower end of the stress levels encountered in devices, and platelet activity state (PAS) was measured in response to six dynamic shear stress waveforms representing repeated passages through a device, and correlated to the predictions of the damage accumulation model. Experimental results demonstrated an increase in PAS with a decrease in “relaxation” time between pulses. The model predictions were in very good agreement with the experimental results.