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Abstract Thromboembolic diseases are a significant cause of mortality and are clinically treated enzymatically with tissue plasminogen activator (tPA). Interestingly, prior studies in fibrin fibers and fibrin gels have demonstrated that thrombolysis may be mechanically sensitive. This study aims to expand mechano‐lytic studies to whole blood clots. Furthermore, this study investigates not only how mechanics impacts lysis but also how lysis impacts mechanics. Therefore, clots made from whole human blood are exposed to tPA while the clots are either stretched or unstretched. After, the resulting degree of clot lysis is measured by weighing the clots and by measuring the concentration of D‐dimer in the surrounding bath. Additionally, each clot's mechanical properties are measured. This study finds that mechanical stretch accelerates loss in clot weight but does not impact the lysis rate as measured by D‐dimer. Moreover, lysis not only removes clot volume but also reduces the remaining clot's stiffness and toughness. In summary, tPA‐induced lysis of whole clot appears mechanically insensitive, but stretch reduces clot weight. Furthermore, results show that thrombolysis weakens clot. This suggests that thrombolysis may increase the risk of secondary embolizations but may also ease clot removal during thrombectomy, for example. 

Abstract Tissue mimicking materials are designed to represent real tissue in applications such as medical device testing and surgical training. Thanks to progress in 3D‐printing technology, tissue mimics can now be easily cast into arbitrary geometries and manufactured with adjustable material properties to mimic a wide variety of tissue types. However, it is unclear how well 3D‐printable mimics represent real tissues and their mechanics. The objective of this work is to fill this knowledge gap using the Stratasys Digital Anatomy 3D‐Printer as an example. To this end, we created mimics of biological tissues we previously tested in our laboratory: blood clots, myocardium, and tricuspid valve leaflets. We printed each tissue mimic to have the identical geometry to its biological counterpart and tested the samples using identical protocols. In our evaluation, we focused on the stiffness of the tissues and their fracture toughness in the case of blood clots. We found that the mechanical behavior of the tissue mimics often differed substantially from the biological tissues they aim to represent. Qualitatively, tissue mimics failed to replicate the traditional strain‐stiffening behavior of soft tissues. Quantitatively, tissue mimics were stiffer than their biological counterparts, especially at small strains, in some cases by orders of magnitude. In those materials in which we tested toughness, we found that tissue mimicking materials were also much tougher than their biological counterparts. Thus, our work highlights limitations of at least one 3D‐printing technology in its ability to mimic the mechanical properties of biological tissues. Therefore, care should be taken when using this technology, especially where tissue mimicking materials are expected to represent soft tissue properties quantitatively. Whether other technologies fare better remains to be seen. 

Abstract Studying and quantifying the mechanics of blood clots is essential to better diagnosis and prognosis of, as well as therapy for, thromboembolic pathologies such as strokes, heart attacks, and pulmonary embolisms. Unfortunately, mechanically testing blood clots is complicated by their softness and fragility, thus making the use of classic mounting techniques, such as clamping, challenging. This is particularly true for mechanical testing under large deformation. Here, we describe protocols for creating in vitro blood clots and securely mounting these samples on mechanical test equipment. To this end, we line 3D‐printed molds with a hook‐and‐loop fabric that, after coagulation, provides a secure interface between the sample and device mount. In summary, our molding and mounting protocols are ideal for performing large‐deformation mechanical testing, with samples that can withstand substantial deformation without delaminating from the apparatus. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Cube‐shaped blood clot preparation Basic Protocol 2: Sheet‐shaped blood clot preparation 

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load–displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load–displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.