Islet-on-chip: promotion of islet health and function via encapsulation within a polymerizable fibrillar collagen scaffold.

The protection and interrogation of pancreatic ß-cell health and function ex vivo is a fundamental aspect of diabetes research, including mechanistic studies, evaluation of ß-cell health modulators, and development and quality control of replacement ß-cell populations. However, present-day islet culture formats, including traditional suspension culture as well as many recently developed microfluidic devices, suspend islets in a liquid microenvironment, disrupting mechanochemical signaling normally found in vivo and limiting ß-cell viability and function in vitro. Herein, we present a novel three-dimensional (3D) microphysiological system (MPS) to extend islet health and function ex vivo by incorporating a polymerizable collagen scaffold to restore biophysical support and islet-collagen mechanobiological cues. Informed by computational models of gas and molecular transport relevant to ß-cell physiology, a MPS configuration was down-selected based on simulated oxygen and nutrient delivery to collagen-encapsulated islets, and 3D-printing was applied as a readily accessible, low-cost rapid prototyping method. Recreating critical aspects of the in vivo microenvironment within the MPS via perfusion and islet-collagen interactions mitigated post-isolation ischemia and apoptosis in mouse islets over a 5-day period. In contrast, islets maintained in traditional suspension formats exhibited progressive hypoxic and apoptotic cores. Finally, dynamic glucose-stimulated insulin secretion measurements were performed on collagen-encapsulated mouse islets in the absence and presence of well-known chemical stressor thapsigargin using the MPS platform and compared to conventional protocols involving commercial perifusion machines. Overall, the MPS described here provides a user-friendly islet culture platform that not only supports long-term ß-cell health and function but also enables multiparametric evaluations.

Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Development of electrochemical Zn2+ sensors for rapid voltammetric detection of glucose-stimulated insulin release from pancreatic ß-cells.

Diabetes is a chronic disease characterized by elevated blood glucose levels resulting from absent or ineffective insulin release from pancreatic ß-cells. ß-cell function is routinely assessed in vitro using static or dynamic glucose-stimulated insulin secretion (GSIS) assays followed by insulin quantification via time-consuming, costly enzyme-linked immunosorbent assays (ELISA). In this study, we developed a highly sensitive electrochemical sensor for zinc (Zn2+), an ion co-released with insulin, as a rapid and low-cost method for measuring dynamic insulin release. Different modifications to glassy carbon electrodes (GCE) were evaluated to develop a sensor that detects physiological Zn2+ concentrations while operating within a biological Krebs Ringer Buffer (KRB) medium (pH 7.2). Electrodeposition of bismuth and indium improved Zn2+ sensitivity and limit of detection (LOD), and a Nafion coating improved selectivity. Using anodic stripping voltammetry (ASV) with a pre-concentration time of 6 min, we achieved a LOD of 2.3 µg/L over the wide linear range of 2.5-500 µg/L Zn2+. Sensor performance improved with 10-min pre-concentration, resulting in increased sensitivity, lower LOD (0.18 µg/L), and a bilinear response over the range of 0.25-10 µg/L Zn2+. We further characterized the physicochemical properties of the Zn2+ sensor using scanning electron microscopy (SEM), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). Finally, we demonstrated the sensor's capability to measure Zn2+ release from glucose-stimulated INS-1 ß-cells and primary mouse islets. Our results exhibited a high correlation with secreted insulin and validated the sensor's potential as a rapid alternative to conventional two-step GSIS plus ELISA methods.

Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through the Gaussian Process. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.