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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.

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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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Background: Accurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements. Materials and Methods: Adaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements. Results: The adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL). Conclusions: Regulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.

Background: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic=hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data. Methods: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-= intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA). Results: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more. Conclusions: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic= hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.

An integrated online multivariate statistical process monitoring (MSPM), quality prediction, and fault diagnosis framework is developed for batch processes. Batch data from I batches, with J process variables measured at K time points generate a three-way array of size I × K × J. Unfolding this three-way array into a two-way matrix of size IK × J by preserving the variable direction is advantageous for developing online MSPM methods because it does not require estimation of future portions of new batches. Two different multiway partial least squares (MPLS) models are developed. The first model (MPLSV) is developed between the data matrix (IK × J) and the local batch time (or an indicator variable) for online MSPM. The second model (MPLSB) is developed between the rearranged data matrix in the batch direction (I × KJ) and the final quality matrix for online prediction of end-of-batch quality. The problem of discontinuity in process variable measurements due to operation switching (or moving to a different phase) that causes problems in alignment and modeling is addressed. Control limits on variable contribution plots are used to improve fault diagnosis capabilities of the MSPM framework. Case studies from a simulated fed-batch penicillin fermentation illustrate the implementation of the methodology.