Three-dimensional (3D) bioprinting techniques have emerged as the most popular methods to fabricate 3D-engineered tissues; however, there are challenges in simultaneously satisfying the requirements of high cell density (HCD), high cell viability, and fine fabrication resolution. In particular, bioprinting resolution of digital light processing–based 3D bioprinting suffers with increasing bioink cell density due to light scattering. We developed a novel approach to mitigate this scattering-induced deterioration of bioprinting resolution. The inclusion of iodixanol in the bioink enables a 10-fold reduction in light scattering and a substantial improvement in fabrication resolution for bioinks with an HCD. Fifty-micrometer fabrication resolution was achieved for a bioink with 0.1 billion per milliliter cell density. To showcase the potential application in tissue/organ 3D bioprinting, HCD thick tissues with fine vascular networks were fabricated. The tissues were viable in a perfusion culture system, with endothelialization and angiogenesis observed after 14 days of culture.

Magnetic Resonance Imaging (MRI) is commonly used to assess the health of the lumbar spine and supporting structures. Studies have suggested that fatty infiltration of the posterior lumbar muscles is important in predicting responses to treatment for low back pain. However, methodological differences exist in defining the region of interest (ROI) of a muscle, which limits the ability to compare data between studies. The purpose of this study was to determine reliability and systematic differences within and between two commonly utilized methodologies for ROI definitions of lumbar paraspinal muscle. T2-weighted MRIs of the mid-L4 vertebrae from 37 patients with low back pain who were scheduled for lumbar spine surgery were included from a hospital database. Fatty infiltration for these patients ranged from low to high, based on Kjaer criteria. Two methods were used to define ROI: 1) segmentation of the multifidus and erector spinae based on fascial planes including epimuscular fat, and 2) segmentation of the multifidus and erector spinae based on visible muscle boundaries, which did not include epimuscular fat. Total cross sectional area (tCSA), fat signal fraction (FSF), muscle cross sectional area, and fat cross sectional area were measured. Degree of agreement between raters for each parameter was assessed using intra-class correlation coefficients (ICC) and area fraction of overlapping voxels. Excellent inter-rater agreement (ICC > 0.75) was observed for all measures for both methods. There was no significant difference between area fraction overlap of ROIs between methods. Method 1 demonstrated a greater tCSA for both the erector spinae (14–15%, p < 0.001) and multifidus (4%, p < 0.016) but a greater FSF only for the erector spinae (11–13%, p < 0.001). The two methods of defining lumbar spine muscle ROIs demonstrated excellent inter-rater reliability, although significant differences exist as method 1 showed larger CSA and FSF values compared to method 2. The results of this study confirm the validity of using either method to measure lumbar paraspinal musculature, and that method should be selected based on the primary outcome variables of interest.

This review discusses key outcome measures for TERM constructs and various imaging techniques that can be used to assess them.

Background: Due to its unique arrangement, the deep and superficial fibers of the multifidus may have differential roles for maintaining spine stabilization and lumbar posture; the superficial multifidus is responsible for lumbar extension and the deep multifidus for intersegmental stability. In patients with chronic lumbar spine pathology, muscle activation patterns have been shown to be attenuated or delayed in the deep, but not superficial, multifidus. This has been interpreted as pain differentially influencing the deep region. However, it is unclear if degenerative changes affecting the composition and function of the multifidus differs between the superficial and deep regions, an alternative explanation for these electrophysiological changes. Therefore, the goal of this study was to investigate macrostructural and microstructural differences between the superficial and deep regions of the multifidus muscle in patients with lumbar spine pathology.Methods: In 16 patients undergoing lumbar spinal surgery for degenerative conditions, multifidus biopsies were acquired at two distinct locations: 1) the most superficial portion of muscle adjacent to the spinous process and 2) approximately 1 cm lateral to the spinous process and deeper at the spinolaminar border of the affected vertebral level. Structural features related to muscle function were histologically compared between these superficial and deep regions, including tissue composition, fat fraction, fiber cross sectional area, fiber type, regeneration, degeneration, vascularity and inflammation.Results: No significant differences in fat signal fraction, muscle area, fiber cross sectional area, muscle regeneration, muscle degeneration, or vascularization were found between the superficial and deep regions of the multifidus. Total collagen content between the two regions was the same. However, the superficial region of the multifidus was found to have less loose and more dense collagen than the deep region.Conclusions: The results of our study did not support that the deep region of the multifidus is more degenerated in patients with lumbar spine pathology, as gross degenerative changes in muscle microstructure and macrostructure were the same in the superficial and deep regions of the multifidus. In these patients, the multifidus is not protected in order to maintain mobility and structural stability of the spine.

The ability to precisely control a scaffold’s microstructure and geometry with light-based 3D printing has been widely demonstrated. However, the modulation of scaffold’s mechanical properties through prescribed printing parameters is still underexplored. This study demonstrates a novel 3D-printing workflow to create a complex, elastomeric scaffold with precision engineered stiffness control by utilizing machine learning. Various printing parameters including the exposure time, light intensity, printing infill, laser pump current, and printing speed were modulated to print poly (glycerol sebacate) acrylate (PGSA) scaffolds with mechanical properties ranging from 49.3 ± 3.3 kPa to 2.78 ± 0.3 MPa. This enables flexibility in spatial stiffness modulation in addition to high resolution scaffold fabrication. Then, a neural network-based machine learning model was developed and validated to optimize printing parameters to yield scaffolds with user-defined stiffness modulation for two different vat photopolymerization methods: a digital light processing (DLP)-based 3D printer was utilized to rapidly fabricate stiffness modulated scaffolds with features on the hundreds of microns scale and a two-photon polymerization (2PP) 3D printer was utilized to print fine structures on the sub-micron scale. A novel 3D-printing workflow was designed to utilize both DLP-based and 2PP 3D printers to create multi-scale scaffolds with precision tuned stiffness control over both gross and fine geometric features. The described workflow can be used to fabricate scaffolds for a variety of tissue engineering applications, specifically for interfacial tissue engineering for which adjacent tissues possess heterogeneous mechanical properties (e.g. muscle-tendon).