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The recent proliferation of three dimensional (3D) printing technologies has allowed the exploration of increasing complex designs, and, furthermore, the consideration of 3D printed constructs for biological applications. However, there is an unmet need for a consistent set of tools for the design and evaluation of these biological 3D printed constructs, particularly as they relate to engineered tissues. For example, identifying the most advantageous construct parameters for the rapid vascularization of an engineered tissue - a critical parameter in regenerative medicine - is difficult without a common group of measures. We demonstrate here a toolbox to design, characterize, and evaluate 3D printed scaffolds for vascularized tissue regenerative medicine. Our toolbox (1) identifies the range of design specifications using a modular design, (2) nondestructively compares the 3D printed scaffolds to the design, (3) evaluates biocompatibility and mechanical properties, and (4) predicts host vessel integration. As a case study, we designed, fabricated, and evaluated polymer scaffolds using a poly(propylene fumarate) based resin. Our work highlights the potential for these tools to be combined as a consistent methodology for the evaluation of porous 3D printed constructs for regenerative medicine.

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.

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