Model-Directed Design of Tissue Engineering Scaffolds

Harnessing the power of machine learning into tissue engineering: current progress and future prospects

Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.

A computational framework to optimize the mechanical behavior of synthetic vascular grafts

The failure of synthetic small-diameter vascular grafts has been attributed to a mismatch in the compliance between the graft and native artery, driving mechanisms that promote thrombosis and neointimal hyperplasia. Additionally, the buckling of grafts results in large deformations that can lead to device failure. Although design features can be added to lessen the buckling potential (e.g., reinforcing coil), the addition is detrimental to decreasing compliance. Herein, we developed a novel finite element (FE) framework to inform vascular graft design by evaluating compliance and resistance to buckling. A batch-processing scheme iterated across the multi-dimensional design parameter space, which included three parameters: coil thickness, modulus, and spacing - generating 100 unique designs. FE models were created for each coil-reinforced graft design to simulate pressurization, axial buckling, and bent buckling, and results were analyzed to quantify compliance, buckling load, and kink radius, respectively. Validation of the FE models demonstrated that model predictions agreed with experimental observations for compliance (r = 0.99), buckling load (r = 0.89), and kink resistance (r = 0.97). Model predictions demonstrated a broad range of values for compliance (1.1-7.9 %/mmHg × 10-2), buckling load (0.28-0.84 N), and kink radius (6-10 mm) across the design parameter space. Subsequently, data for each design parameter combination were optimized (i.e., minimized) to identify candidate graft designs with promising mechanical properties. Our model-directed framework successfully elucidated the complex mechanical determinants of graft performance, established structure-property relationships, and identified vascular graft designs with optimal mechanical properties, potentially improving clinical outcomes by addressing device failure.

Navigating the challenges and exploring the perspectives associated with emerging novel biomaterials

The field of biomaterials is a continuously evolving interdisciplinary field encompassing biological sciences, materials sciences, chemical sciences, and physical sciences with a multitude of applications realized every year. However, different biomaterials developed for different applications have unique challenges in the form of biological barriers, and addressing these challenges simultaneously is also a challenge. Nevertheless, immense progress has been made through the development of novel materials with minimal adverse effects such as DNA nanostructures, specific synthesis strategies based on supramolecular chemistry, and modulating the shortcomings of existing biomaterials through effective functionalization techniques. This review discusses all these aspects of biomaterials, including the challenges at each level of their development and application, proposed countermeasures for these challenges, and some future directions that may have potential benefits.

Structural DNA nanotechnology at the nexus of next-generation bio-applications: challenges and perspectives

DNA nanotechnology has significantly progressed in the last four decades, creating nucleic acid structures widely used in various biological applications. The structural flexibility, programmability, and multiform customization of DNA-based nanostructures make them ideal for creating structures of all sizes and shapes and multivalent drug delivery systems. Since then, DNA nanotechnology has advanced significantly, and numerous DNA nanostructures have been used in biology and other scientific disciplines. Despite the progress made in DNA nanotechnology, challenges still need to be addressed before DNA nanostructures can be widely used in biological interfaces. We can open the door for upcoming uses of DNA nanoparticles by tackling these issues and looking into new avenues. The historical development of various DNA nanomaterials has been thoroughly examined in this review, along with the underlying theoretical underpinnings, a summary of their applications in various fields, and an examination of the current roadblocks and potential future directions.