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Rotator cuff tears (RCTs) are one of the primary causes of shoulder pain and dysfunction in the upper extremity accounting over 4.5 million physician visits per year with 250,000 rotator cuff repairs being performed annually in the U.S. While the tear is often considered an injury to a specific tendon/tendons and consequently treated as such, there are secondary effects of RCTs that may have significant consequences for shoulder function. Specifically, RCTs have been shown to affect the joint cartilage, bone, the ligaments, as well as the remaining intact tendons of the shoulder joint. Injuries associated with the upper extremities account for the largest percent of workplace injuries. Unfortunately, the variable success rate related to RCTs motivates the need for a better understanding of the biomechanical consequences associated with the shoulder injuries. Understanding the timing of the injury and the secondary anatomic consequences that are likely to have occurred are also of great importance in treatment planning because the approach to the treatment algorithm is influenced by the functional and anatomic state of the rotator cuff and the shoulder complex in general. In this review, we summarized the contribution of RCTs to joint stability in terms of both primary (injured tendon) and secondary (remaining tissues) consequences including anatomic changes in the tissues surrounding the affected tendon/tendons. The mechanical basis of normal shoulder joint function depends on the balance between active muscle forces and passive stabilization from the joint surfaces, capsular ligaments, and labrum. Evaluating the role of all tissues working together as a system for maintaining joint stability during function is important to understand the effects of RCT, specifically in the working population, and may provide insight into root causes of shoulder injury.

The freeform generation of active electronics can impart advanced optical, computational, or sensing capabilities to an otherwise passive construct by overcoming the geometrical and mechanical dichotomies between conventional electronics manufacturing technologies and a broad range of three-dimensional (3D) systems. Previous work has demonstrated the capability to entirely 3D print active electronics such as photodetectors and light-emitting diodes by leveraging an evaporation-driven multi-scale 3D printing approach. However, the evaporative patterning process is highly sensitive to print parameters such as concentration and ink composition. The assembly process is governed by the multiphase interactions between solutes, solvents, and the microenvironment. The process is susceptible to environmental perturbations and instability, which can cause unexpected deviation from targeted print patterns. The ability to print consistently is particularly important for the printing of active electronics, which require the integration of multiple functional layers. Here we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition and classify complex internal features. Specifically, the microfluidic-driven 3D printer can rapidly modulate ink composition, such as concentration and solvent-to-cosolvent ratio, to explore multi-dimensional parameter space. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in-situ pattern classification. We envision that such integration will provide valuable insights in understanding the complex evaporative-driven assembly process and ultimately enable an autonomous optimisation of printing parameters that can robustly adapt to unexpected perturbations.