Over 300,000 heart valve replacements are performed annually to replace stenotic and regurgitant heart valves. Bioprosthetic heart valves (BHVs), derived from glutaraldehyde crosslinked (GLUT) porcine aortic valve leaflets or bovine pericardium are often used. However, valve failure can occur within 12–15 years due to calcification and/or progressive degeneration. In this study, we have developed a novel fabrication method that utilizes carbodiimide, neomycin trisulfate, and pentagalloyl glucose crosslinking chemistry (TRI) to better stabilize the extracellular matrix of porcine aortic valve leaflets. We demonstrate that TRI treated leaflets show similar biomechanics to GLUT crosslinked leaflets. TRI treated leaflets had better resistance to enzymatic degradation in vitro and demonstrated better tearing toughness after challenged with enzymatic degradation. When implanted subcutaneously in rats for up to 90 days, GLUT control leaflets calcified heavily while TRI treated leaflets resisted calcification, retained more ECM components, and showed better biocompatibility.

Bioprosthetic heart valves (BHVs), derived from glutaraldehyde crosslinked (GLUT) porcine aortic valve leaflets or bovine pericardium (BP), are used to replace defective heart valves. However, valve failure can occur within 12–15 years due to calcification and/or progressive structural degeneration. We present a novel fabrication method that utilizes carbodiimide, neomycin trisulfate, and pentagalloyl glucose crosslinking chemistry (TRI) to better stabilize the extracellular matrix of BP. We demonstrate that TRI-treated BP is more compliant than GLUT-treated BP. GLUT-treated BP exhibited permanent geometric deformation and complete alteration of apparent mechanical properties when subjected to induced static strain. TRI BP, on the other hand, did not exhibit such permanent geometric deformations or significant alterations of apparent mechanical properties. TRI BP also exhibited better resistance to enzymatic degradation in vitro and calcification in vivo when implanted subcutaneously in juvenile rats for up to 30 days.

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The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.

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