Engineering collagenous analogs of connective tissue extracellular matrix.

Connective tissue extracellular matrix (ECM) consists of an interwoven network of contiguous collagen fibers that regulate cell activity, direct biological function, and guide tissue homeostasis throughout life. Recently, ECM analogs have emerged as a unique ex vivo culture platform for studying healthy and diseased tissues and in the latter, enabling the screening for and development of therapeutic regimen. Since these tissue models can mitigate the concern that observations from animal models do not always translate clinically, the design and production of a collagenous ECM analogue with relevant chemistry and nano- to micro-scale architecture remains a frontier challenge in the field. Therefore, the objectives of this study are two-fold- first, to apply green electrospinning approaches to the fabrication of an ECM analog with nanoscale mimicry and second, to systematically optimize collagen crosslinking in order to produce a stable, collagen-like substrate with continuous fibrous architecture that supports human cell culture and phenotypic expression. Specifically, the "green" electrospinning solvent acetic acid was evaluated for biofabrication of gelatin-based meshes, followed by the optimization of glutaraldehyde (GTA) crosslinking under controlled ambient conditions. These efforts led to the production of a collagen-like mesh with nano- and micro-scale cues, fibrous continuity with little batch-to-batch variability, and proven stability in both dry and wet conditions. Moreover, the as-fabricated mesh architecture and native chemistry were preserved with augmented mechanical properties. These meshes supported the in vitro expansion of stem cells and the production of a mineralized matrix by human osteoblast-like cells. Collectively these findings demonstrate the potential of green fabrication in the production of a collagen-like ECM analog with physiological relevance. Future studies will explore the potential of this high-fidelity platform for elucidating cell-matrix interactions and their relevance in connective tissue healing.

Green electrospinning for biomaterials and biofabrication.

Green manufacturing has emerged across industries, propelled by a growing awareness of the negative environmental and health impacts associated with traditional practices. In the biomaterials industry, electrospinning is a ubiquitous fabrication method for producing nano- to micro-scale fibrous meshes that resemble native tissues, but this process traditionally utilizes solvents that are environmentally hazardous and pose a significant barrier to industrial scale-up and clinical translation. Applying sustainability principles to biomaterial production, we have developed a 'green electrospinning' process by systematically testing biologically benign solvents (U.S. Food and Drug Administration Q3C Class 3), and have identified acetic acid as a green solvent that exhibits low ecological impact (global warming potential (GWP) = 1.40 CO2eq. kg/L) and supports a stable electrospinning jet under routine fabrication conditions. By tuning electrospinning parameters, such as needle-plate distance and flow rate, we updated the fabrication of widely utilized biomedical polymers (e.g. poly-α-hydroxyesters, collagen), polymer blends, polymer-ceramic composites, and growth factor delivery systems. Resulting 'green' fibers and composites are comparable to traditional meshes in terms of composition, chemistry, architecture, mechanical properties, and biocompatibility. Interestingly, material properties of green synthetic fibers are more biomimetic than those of traditionally electrospun fibers, doubling in ductility (91.86 ± 35.65 vs. 45 ± 15.07%,n= 10,p< 0.05) without compromising yield strength (1.32 ± 0.26 vs. 1.38 ± 0.32 MPa) or ultimate tensile strength (2.49 ± 0.55 vs. 2.36 ± 0.45 MPa). Most importantly, green electrospinning proves advantageous for biofabrication, rendering a greater protection of growth factors during fiber formation (72.30 ± 1.94 vs. 62.87 ± 2.49% alpha helical content,n= 3,p< 0.05) and recapitulating native ECM mechanics in the fabrication of biopolymer-based meshes (16.57 ± 3.92% ductility, 33.38 ± 30.26 MPa elastic modulus, 1.30 ± 0.19 MPa yield strength, and 2.13 ± 0.36 MPa ultimate tensile strength,n= 10). The eco-conscious approach demonstrated here represents a paradigm shift in biofabrication, and will accelerate the translation of scalable biomaterials and biomimetic scaffolds for tissue engineering and regenerative medicine.

Darwin's Neural Network: AI-based Strategies for Rapid and Scalable Cell and Coronavirus Screening.

Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images. Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data through use in conjunction with biological imaging modalities. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro.