Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering

Advances in non-invasive biosensing measures to monitor wound healing progression

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed

Loss of \u3ci\u3eActa2\u3c/i\u3e in Cardiac Fibroblasts Does Not Prevent the Myofibroblast Differentiation or Affect the Cardiac Repair After Myocardial Infarction

In response to myocardial infarction (MI), quiescent cardiac fibroblasts differentiate into myofibroblasts mediating tissue repair. One of the most widely accepted markers of myofibroblast differentiation is the expression of Acta2 which encodes smooth muscle alpha-actin (SMαA) that is assembled into stress fibers. However, the requirement of Acta2/SMαA in the myofibroblast differentiation of cardiac fibroblasts and its role in post-MI cardiac repair remained unknown. To answer these questions, we generated a tamoxifen-inducible cardiac fibroblast-specific Acta2 knockout mouse line. Surprisingly, mice that lacked Acta2 in cardiac fibroblasts had a normal post-MI survival rate. Moreover, Acta2 deletion did not affect the function or histology of infarcted hearts. No difference was detected in the proliferation, migration, or contractility between WT and Acta2-null cardiac myofibroblasts. Acta2-null cardiac myofibroblasts had a normal total filamentous actin level and total actin level. Acta2 deletion caused a significant compensatory increase in the transcription level of non-Acta2 actin isoforms, especially Actg2 and Acta1. Moreover, in myofibroblasts, the transcription levels of cytoplasmic actin isoforms were significantly higher than those of muscle actin isoforms. In addition, we found that myocardin-related transcription factor-A is critical for myofibroblast differentiation but is not required for the compensatory effects of non-Acta2 isoforms. In conclusion, the Acta2 deletion does not prevent the myofibroblast differentiation of cardiac fibroblasts or affect the post-MI cardiac repair, and the increased expression and stress fiber formation of non-SMαA actin isoforms and the functional redundancy between actin isoforms are able to compensate for the loss of Acta2 in cardiac myofibroblasts

Engineering Extracellular Matrix Proteins to Enhance Cardiac Regeneration After Myocardial Infarction

Engineering microenvironments for accelerated myocardial repair is a challenging goal. Cell therapy has evolved over a few decades to engraft therapeutic cells to replenish lost cardiomyocytes in the left ventricle. However, compelling evidence supports that tailoring specific signals to endogenous cells rather than the direct integration of therapeutic cells could be an attractive strategy for better clinical outcomes. Of many possible routes to instruct endogenous cells, we reviewed recent cases that extracellular matrix (ECM) proteins contribute to enhanced cardiomyocyte proliferation from neonates to adults. In addition, the presence of ECM proteins exerts biophysical regulation in tissue, leading to the control of microenvironments and adaptation for enhanced cardiomyocyte proliferation. Finally, we also summarized recent clinical trials exclusively using ECM proteins, further supporting the notion that engineering ECM proteins would be a critical strategy to enhance myocardial repair without taking any risks or complications of applying therapeutic cardiac cells

Distribution of polymeric nanoparticles in the eye: implications in ocular disease therapy

Advantages of polymeric nanoparticles as drug delivery systems include controlled release, enhanced drug stability and bioavailability, and specific tissue targeting. Nanoparticle properties such as hydrophobicity, size, and charge, mucoadhesion, and surface ligands, as well as administration route and suspension media affect their ability to overcome ocular barriers and distribute in the eye, and must be carefully designed for specific target tissues and ocular diseases. This review seeks to discuss the available literature on the biodistribution of polymeric nanoparticles and discuss the effects of nanoparticle composition and administration method on their ocular penetration, distribution, elimination, toxicity, and efficacy, with potential impact on clinical applications

An integrated statistical model for enhanced murine cardiomyocyte differentiation via optimized engagement of 3D extracellular matrices

The extracellular matrix (ECM) impacts stem cell differentiation, but identifying formulations supportive of differentiation is challenging in 3D models. Prior efforts involving combinatorial ECM arrays seemed intuitively advantageous. We propose an alternative that suggests reducing sample size and technological burden can be beneficial and accessible when coupled to design of experiments approaches. We predict optimized ECM formulations could augment differentiation of cardiomyocytes derived in vitro. We employed native chemical ligation to polymerize 3D poly (ethylene glycol) hydrogels under mild conditions while entrapping various combinations of ECM and murine induced pluripotent stem cells. Systematic optimization for cardiomyocyte differentiation yielded a predicted solution of 61%, 24%, and 15% of collagen type I, laminin-111, and fibronectin, respectively. This solution was confirmed by increased numbers of cardiac troponin T, α-myosin heavy chain and α-sarcomeric actinin-expressing cells relative to suboptimum solutions. Cardiomyocytes of composites exhibited connexin43 expression, appropriate contractile kinetics and intracellular calcium handling. Further, adding a modulator of adhesion, thrombospondin-1, abrogated cardiomyocyte differentiation. Thus, the integrated biomaterial platform statistically identified an ECM formulation best supportive of cardiomyocyte differentiation. In future, this formulation could be coupled with biochemical stimulation to improve functional maturation of cardiomyocytes derived in vitro or transplanted in vivo

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting