Composite guidance scaffolds for neural tissue engineering

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Hyaluronan, neural stem cells and tissue reconstruction after acute ischemic stroke.

Focal stroke is a disabling disease with lifelong sensory, motor and cognitive impairments. Given the paucity of effective clinical treatments, basic scientists are developing novel options for protection of the affected brain and regeneration of lost tissue. Tissue bioengineering and stem/progenitor cell treatments have both been individually pursued for stroke neural repair therapies, with some benefit in tissue recovery. Emerging directions in stroke neural repair approaches combine these two therapies to use biopolymers with stem/progenitor transplants to promote greater cell survival in the transplant and directed delivery of bioactive molecules to the transplanted cells and the adjacent injured tissue. In this review the background literature on a combined use of neural stem/progenitor cells encapsulated in hyaluronan gels is discussed and the way this therapeutic approach can affect the important processes involved in brain tissue reconstruction, such as angiogenesis, axon regeneration, neural differentiation and inflammation is clarified. The glycosaminoglycan hyaluronan can optimize those processes and be employed in a successful neural tissue engineering approach

Decellularised extracellular matrix-derived peptides from neural retina and retinal pigment epithelium enhance the expression of synaptic markers and light responsiveness of human pluripotent stem cell derived retinal organoids

Tissue specific extracellular matrices (ECM) provide structural support and enable access to molecular signals and metabolites, which are essential for directing stem cell renewal and differentiation. To mimic this phenomenon in vitro, tissue decellularisation approaches have been developed, resulting in the generation of natural ECM scaffolds that have comparable physical and biochemical properties of the natural tissues and are currently gaining traction in tissue engineering and regenerative therapies due to the ease of standardised production, and constant availability. In this manuscript we report the successful generation of decellularised ECM-derived peptides from neural retina (decel NR) and retinal pigment epithelium (decel RPE), and their impact on differentiation of human pluripotent stem cells (hPSCs) to retinal organoids. We show that culture media supplementation with decel RPE and RPE-conditioned media (CM RPE) significantly increases the generation of rod photoreceptors, whilst addition of decel NR and decel RPE significantly enhances ribbon synapse marker expression and the light responsiveness of retinal organoids. Photoreceptor maturation, formation of correct synapses between retinal cells and recording of robust light responses from hPSC-derived retinal organoids remain unresolved challenges for the field of regenerative medicine. Enhanced rod photoreceptor differentiation, synaptogenesis and light response in response to addition of decellularised matrices from RPE and neural retina as shown herein provide a novel and substantial advance in generation of retinal organoids for drug screening, tissue engineering and regenerative medicine

Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images

Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. Methods: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. Results: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1 and in the colon, the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. Conclusions: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.Comment: Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS

Biocompatibility of PCL-Graphene Nanostructured Scaffolds with Mouse Embryonic Stem Cell-derived Cardiomyocytes

Since adult cardiomyocytes are not readily available for clinical use, numerous efforts have been made to derive functional cardiomyocytes from pluripotent stem cells. [1,2]. A variety of cardiovascular tissue engineering strategies have been explored to develop engineered cardiac tissues for in vitro and in vivo applications utilizing fibrous tissue scaffolds, both single polymer scaffolds and hybrids of polymers with hydrogels, coatings or embedded materials[3-9]. While graphene, a single layer carbon crystal, has recently become a material of interest for tissue engineering applications including osteogenic, neural and stem cell differentiation [10-12], its potential for cardiac tissue engineering is yet unknown. The inherent electro-activity of the myocardium makes graphene an especially attractive option for cardiac tissue engineering due to its high electrical conductivity. Thus, a novel hybrid 3D scaffold with graphene has been developed and its effect on the function of stem cell derived cardiomyocytes is examined

Effects of Liposomes Contained in Thermosensitive Hydrogels as Biomaterials Useful in Neural Tissue Engineering

Indexación: Scopus.Advances in the generation of suitable thermosensitive hydrogels for the delivery of cells in neural tissue engineering demonstrate a delicate relationship between physical properties and capabilities to promote cell proliferation and differentiation. To improve the properties of these materials, it is possible to add liposomes for the controlled release of bioactive elements, which in turn can affect the physical and biological properties of the hydrogels. In the present investigation, different hydrogels based on Pluronic F127 have been formulated with the incorporation of chitosan and two types of liposomes of two different sizes. The rheological and thermal properties and their relation with the neurite proliferation and growth of the PC12 cell line were evaluated. Our results show that the incorporation of liposomes modifies the properties of the hydrogels dependent on the concentration of chitosan and the lipid type in the liposomes, which directly affect the capabilities of the hydrogels to promote the viability and differentiation of PC12 cells. © 2017 by the authors.http://www.mdpi.com/1996-1944/10/10/112

Nanoengineering Neural Stem Cells on Biomimetic Substrates Using Magnetofection Technology

Tissue engineering studies are witnessing a major paradigm shift to cell culture on biomimetic materials that replicate native tissue features from which the cells are derived. Few studies have been performed in this regard for neural cells, particularly in nanomedicine. For example, platforms such as magnetic nanoparticles (MNPs) have proven efficient as multifunctional tools for cell tracking and genetic engineering of neural transplant populations. However, as far as we are aware, all current studies have been conducted using neural cells propagated on non-neuromimetic substrates that fail to represent the mechano-elastic properties of brain and spinal cord microenvironments. Accordingly, it can be predicted that such data is of less translational and physiological relevance than that derived from cells grown in neuromimetic environments. Therefore, we have performed the first test of magnetofection technology (enhancing MNP delivery using applied magnetic fields with significant potential for therapeutic application) and its utility in genetically engineering neural stem cells (NSCs; a population of high clinical relevance) propagated in biomimetic hydrogels. We demonstrate magnetic field application safely enhances MNP mediated transfection of NSCs grown as 3D spheroid structures in collagen which more closely replicates the intrinsic mechanical and structural properties of neural tissue than routinely used hard substrates. Further, as it is well known that MNP uptake is mediated by endocytosis we also investigated NSC membrane activity grown on both soft and hard substrates. Using high resolution scanning electron microscopy we were able to prove that NSCs display lower levels of membrane activity on soft substrates compared to hard, a finding which could have particular impact on MNP mediated engineering strategies of cells propagated in physiologically relevant systems

Nonlinear Elastic Material Property Estimation of Lower Extremity Residual Limb Tissues

The interface stresses between the residual limb and prosthetic socket have been studied to investigate prosthetic fit. Finite-element models of the residual limb-prosthetic socket interface facilitate investigation of the mechanical interface and may serve as a potential tool for future prosthetic socket design. However, the success of such residual limb models to date has been limited, in large part due to inadequate material formulations used to approximate the mechanical behavior of residual limb soft tissues. Nonlinear finite-element analysis was used to simulate force-displacement data obtained during in vivo rate-controlled (1, 5, and 10 mm/s) cyclic indentation of the residual limb soft tissues of seven individuals with transtibial amputation. The finite-element models facilitated determination of an appropriate set of nonlinear elastic material coefficients for bulk soft tissue at discrete clinically relevant test locations. Axisymmetric finite-element models of the residual limb bulk soft tissue in the vicinity of the test location, the socket wall and the indentor tip were developed incorporating contact analysis, large displacement, and large strain, and the James-Green-Simpson nonlinear elastic material formulation. Model dimensions were based on medical imaging studies of the residual limbs. The material coefficients were selected such that the normalized sum of square error (NSSE) between the experimental and finite-element model indentor tip reaction force was minimized. A total of 95% of the experimental data were simulated using the James-Green-Simpson material formulation with an NSSE less than 5%. The respective James-Green-Simpson material coefficients varied with subject, test location, and indentation rate. Therefore, these coefficients cannot be readily extrapolated to other sites or individuals, or to the same site and individual some time after testing

Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel