Peripheral nerve injuries persist as a major clinical issue facing the US population and can be caused by stretch, laceration, or crush injuries. Small nerve gaps are simple to treat, and the nerve stumps can be reattached with sutures. In longer nerve gaps, traditional treatment options consist of autografts, hollow nerve guidance conduits, and, more recently, manufactured fibrous scaffolds. These manufactured scaffolds often incorporate stem cells, growth factors, and/or extracellular matrix (ECM) proteins to better mimic the native environment but can have issues with homogenous cell distribution or uniformly oriented neurite outgrowth in scaffolds without fibrous alignment. Here, we utilize a custom device to fabricate collagen I hydrogels with aligned fibers and encapsulated adipose-derived mesenchymal stem cells (ASCs) for potential use as a peripheral nerve repair graft. Initial results of our scaffold system revealed significantly less cell viability in higher collagen gel concentrations; 3 mg/mL gels showed 84.8 ± 7.3% viable cells, compared to 6 mg/mL gels viability of 76.7 ± 9.5%. Mechanical testing of the 3 mg/mL gels showed a Young's modulus of 6.5 ± 0.8 kPa nearly matching 7.45 kPa known to support Schwann cell migration. Further analysis of scaffolds coupled with stretching in vitro revealed heightened angiogenic and factor secretion, ECM deposition, fiber alignment, and dorsal root ganglia (DRG) neurite outgrowth along the axis of fiber alignment. Our platform serves as an in vitro testbed to assess neuro-regenerative potential of ASCs in aligned collagen fiber scaffolds and may provide guidance on next-generation nerve repair scaffold design.

This manuscript describes the development of a resources module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Skin undergoes mechanical alterations due to changes in the composition and structure of the collagenous dermis with aging. Previous studies have conflicting findings, with both increased and decreased stiffness reported for aging skin. The underlying structure–function relationships that drive age-related changes are complex and difficult to study individually. One potential contributor to these variations is the accumulation of nonenzymatic crosslinks within collagen fibers, which affect dermal collagen remodeling and mechanical properties. Specifically, these crosslinks make individual fibers stiffer in their plastic loading region and lead to increased fragmentation of the collagenous network. To better understand the influence of these changes, we investigated the impact of nonenzymatic crosslink changes on the dermal microstructure using discrete fiber networks representative of the dermal microstructure. Our findings suggest that stiffening the plastic region of collagen's mechanical response has minimal effects on network-level stiffness and failure stresses. Conversely, simulating fragmentation through a loss of connectivity substantially reduces network stiffness and failure stress, while increasing stretch ratios at failure.

Skin is a complex tissue whose biomechanical properties are generally understood in terms of an incompressible material whose microstructure undergoes affine deformations. A growing number of experiments, however, have demonstrated that skin has a high Poisson's ratio, substantially decreases in volume during uniaxial tensile loading, and demonstrates collagen fiber kinematics that are not affine with local deformation. In order to better understand the mechanical basis for these properties, we constructed multiscale mechanical models (MSM) of mouse skin based on microstructural multiphoton microscopy imaging of the dermal microstructure acquired during mechanical testing. Three models that spanned the cases of highly aligned, moderately aligned, and nearly random fiber networks were examined and compared to the data acquired from uniaxially stretched skin. Our results demonstrate that MSMs consisting of networks of matched fiber organization can predict the biomechanical behavior of mouse skin, including the large decrease in tissue volume and nonaffine fiber kinematics observed under uniaxial tension.