Inflammation-induced subcutaneous neovascularization for the long-term survival of encapsulated islets without immunosuppression.

Cellular therapies for type-1 diabetes can leverage cell encapsulation to dispense with immunosuppression. However, encapsulated islet cells do not survive long, particularly when implanted in poorly vascularized subcutaneous sites. Here we show that the induction of neovascularization via temporary controlled inflammation through the implantation of a nylon catheter can be used to create a subcutaneous cavity that supports the transplantation and optimal function of a geometrically matching islet-encapsulation device consisting of a twisted nylon surgical thread coated with an islet-seeded alginate hydrogel. The neovascularized cavity led to the sustained reversal of diabetes, as we show in immunocompetent syngeneic, allogeneic and xenogeneic mouse models of diabetes, owing to increased oxygenation, physiological glucose responsiveness and islet survival, as indicated by a computational model of mass transport. The cavity also allowed for the in situ replacement of impaired devices, with prompt return to normoglycemia. Controlled inflammation-induced neovascularization is a scalable approach, as we show with a minipig model, and may facilitate the clinical translation of immunosuppression-free subcutaneous islet transplantation.

Tissue poromechanical deformation effects on steam pop likelihood in 3-D radiofrequency cardiac ablation.

Radiofrequency Cardiac Ablation (RFCA) is a common procedure that heats cardiac tissue to destroy abnormal signal pathways to eliminate arrhythmias. The complex multiphysics phenomena during this procedure need to be better understood to improve both procedure and device design. A deformable poromechanical model of cardiac tissue was developed that coupled joule heating from the electrode, heat transfer, and blood flow from normal perfusion and thermally driven natural convection, which mimics the real tissue structure more closely and provides more realistic results compared to previous models. The expansion of tissue from temperature rise reduces blood velocity, leading to increased tissue temperature, thus affecting steam pop occurrence. Detailed temperature velocity, and thermal expansion of the tissue provided a comprehensive picture of the process. Poromechanical expansion of the tissue from temperature rise reduces blood velocity, increasing tissue temperature. Tissue properties influence temperatures, with lower porosity increasing the temperatures slightly, due to lower velocities. Deeper electrode insertion raises temperature due to increased current flow. The results demonstrate that a 5% increase in porosity leads to a considerable 10% increase in maximum tissue temperature. These insights should greatly help in avoiding undesirable heating effects that can lead to steam pop and in designing improved electrodes.

A predictive computational platform for optimizing the design of bioartificial pancreas devices.

The delivery of encapsulated islets or stem cell-derived insulin-producing cells (i.e., bioartificial pancreas devices) may achieve a functional cure for type 1 diabetes, but their efficacy is limited by mass transport constraints. Modeling such constraints is thus desirable, but previous efforts invoke simplifications which limit the utility of their insights. Herein, we present a computational platform for investigating the therapeutic capacity of generic and user-programmable bioartificial pancreas devices, which accounts for highly influential stochastic properties including the size distribution and random localization of the cells. We first apply the platform in a study which finds that endogenous islet size distribution variance significantly influences device potency. Then we pursue optimizations, determining ideal device structures and estimates of the curative cell dose. Finally, we propose a new, device-specific islet equivalence conversion table, and develop a surrogate machine learning model, hosted on a web application, to rapidly produce these coefficients for user-defined devices.

Engineering modeling frameworks for microbial food safety at various scales.

The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such model-based approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer). A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.

An inverse-breathing encapsulation system for cell delivery.

Cell encapsulation represents a promising therapeutic strategy for many hormone-deficient diseases such as type 1 diabetes (T1D). However, adequate oxygenation of the encapsulated cells remains a challenge, especially in the poorly oxygenated subcutaneous site. Here, we present an encapsulation system that generates oxygen (O2) for the cells from their own waste product, carbon dioxide (CO2), in a self-regulated (i.e., "inverse breathing") way. We leveraged a gas-solid (CO2-lithium peroxide) reaction that was completely separated from the aqueous cellular environment by a gas permeable membrane. O2 measurements and imaging validated CO2-responsive O2 release, which improved cell survival in hypoxic conditions. Simulation-guided optimization yielded a device that restored normoglycemia of immunocompetent diabetic mice for over 3 months. Furthermore, functional islets were observed in scaled-up device implants in minipigs retrieved after 2 months. This inverse breathing device provides a potential system to support long-term cell function in the clinically attractive subcutaneous site.

A Mechanistic Model for Bacterial Retention and Infiltration on a Leaf Surface during a Sessile Droplet Evaporation.

Evaporation of sessile droplets on the surface of plant leaves is a process that frequently occurs during plant growth as well as postharvest processes. Evaporation-driven internal flows within sessile droplets can transport microorganisms near the leaf surface, facilitating their adhesion to surface microstructures such as trichomes, and infiltration into available openings such as stomata and grooves. A mechanistic model for this retention and infiltration pathway was developed. Solution domain is a sessile droplet located on a leaf surface, as well as its surrounding gas. The model includes fluid flow within the droplet and gas phases, gas-water interface tracking, heat transfer, transport of vapor in gas, and transport of sugar and bacteria within water. The model results are validated based on available literature data and experimental images. The results showed that a hydrophilic surface would promote bacterial retention and infiltration. Evaporation-driven flows increase concentration of bacteria around or inside microstructures at the leaf surface, facilitating their adhesion and infiltration. Larger microstructures having wider spacing between them increased the retention. A higher evaporation rate led to higher infiltration. Chemotaxis toward nutrients at the leaf surface and random motility were shown to decrease the retention and infiltration during evaporation.

Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms.

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Mechanistic modeling of light-induced chemotactic infiltration of bacteria into leaf stomata.

Light is one of the factors that can play a role in bacterial infiltration into leafy greens by keeping stomata open and providing photosynthetic products for microorganisms. We model chemotactic transport of bacteria within a leaf tissue in response to photosynthesis occurring within plant mesophyll. The model includes transport of carbon dioxide, oxygen, bicarbonate, sucrose/glucose, bacteria, and autoinducer-2 within the leaf tissue. Biological processes of carbon fixation in chloroplasts, and respiration in mitochondria of the plant cells, as well as motility, chemotaxis, nutrient consumption and communication in the bacterial community are considered. We show that presence of light is enough to boost bacterial chemotaxis through the stomatal opening and toward photosynthetic products within the leaf tissue. Bacterial chemotactic ability is a major player in infiltration, and plant stomatal defense in closing the stomata as a perception of microbe-associated molecular patterns is an effective way to inhibit the infiltration.

Mass production of shaped particles through vortex ring freezing.

A vortex ring is a torus-shaped fluidic vortex. During its formation, the fluid experiences a rich variety of intriguing geometrical intermediates from spherical to toroidal. Here we show that these constantly changing intermediates can be 'frozen' at controlled time points into particles with various unusual and unprecedented shapes. These novel vortex ring-derived particles, are mass-produced by employing a simple and inexpensive electrospraying technique, with their sizes well controlled from hundreds of microns to millimetres. Guided further by theoretical analyses and a laminar multiphase fluid flow simulation, we show that this freezing approach is applicable to a broad range of materials from organic polysaccharides to inorganic nanoparticles. We demonstrate the unique advantages of these vortex ring-derived particles in several applications including cell encapsulation, three-dimensional cell culture, and cell-free protein production. Moreover, compartmentalization and ordered-structures composed of these novel particles are all achieved, creating opportunities to engineer more sophisticated hierarchical materials.

Modeling moisture migration in a multi-domain food system: Application to storage of a sandwich system.

Moisture transport in a food system involving two different materials of unequal moisture content was modeled with water activity as the driving force using a porous media framework. This model was applied to a bread-barbecue chicken pocket sandwich stored in isothermal conditions. The model successfully predicted the equilibrium condition, where the two materials, bread and chicken, reached the same water activity, but not the same water content. The transient changes in the moisture content in the bread and chicken were predicted and shown to be significantly affected by air gap between the bread and chicken. The prediction process was also sensitive to the Darcy permeability values for the materials. The modeling framework presented for a sandwich system is very general and can easily be extended to other multicomponent food systems.

Soft matter approaches as enablers for food macroscale simulation.

Macroscopic deformable multiphase porous media models have been successful in describing many complex food processes. However, the properties needed for such detailed physics-based models are scarce and consist of primarily empirical models obtained from experiment. Likewise, driving forces such as swelling pressure have also been approached empirically, without physics-based explanations or prediction capabilities. Soft matter based prediction of properties will provide an additional avenue to obtaining properties and also provide a deeper and critical understanding of how these properties change with composition, temperature and other process variables.

Experimental and analytical temperature distributions during oven-based convection heating.

Mathematical models, combined with experimental evaluation, provide an approach to understand, design, and optimize food process operations. Magnetic resonance imaging (MRI), as an experimental technique, is used extensively in both medical and engineering applications to measure and quantify transport processes. Magnetic resonance (MR) was used in this study to assess a mathematical model based on Fourier's second law. The objective was to compare analytical solutions for the prediction of internal temperature distributions in foods during oven-based convective heating to experimental temperature measurements and determine at what point during the heating process a coupled heat and mass transport process should be considered. Cylindrical samples of a model food gel, Russet potato and rehydrated mashed potato were heated in a convection oven for specified times. Experimentally measured internal temperatures were compared to the internal temperatures predicted by the analytical model. Temperatures distributions in the axial direction compared favorably for the gel and acceptably for the Russet and mashed potato samples. The MR-acquired temperatures in the radial direction for the gel resulted in a shallower gradient than predicted but followed the expected trend. For the potato samples, the MR-acquired temperatures in the radial direction were not qualitatively similar to the analytical predictions due to moisture loss during heating. If temperature resolution is required in the radial direction, moisture losses merit the use of transport models that couple heat and mass transfer.

Simulation of turbulent airflow using a CT based upper airway model of a racehorse.

Computational model for airflow through the upper airway of a horse was developed. Previous flow models for human airway do not hold true for horses due to significant differences in anatomy and the high Reynolds number of flow in the equine airway. Moreover, models that simulate the entire respiratory cycle and emphasize on pressures inside the airway in relation to various anatomical diseases are lacking. The geometry of the airway was created by reconstructing images obtained from computed tomography scans of a thoroughbred racehorse. Different geometries for inhalation and exhalation were used for the model based on the difference in the nasopharynx size during the two phases of respiration. The Reynolds averaged Navier-Stokes equations were solved for the isothermal flow with the standard k-epsilon model for turbulence. Transient pressure boundary conditions for the entire breathing cycle were obtained from past experimental studies on live horses. The flow equations were solved in a commercial finite volume solver. The flow rates, computed based on the applied pressure conditions, were compared to experimentally measured flow rates for model validation. Detailed analysis of velocity, pressure, and turbulence characteristics of the flow was done. Velocity magnitudes at various slices during inhalation were found to be higher than corresponding velocity magnitudes during exhalation. The front and middle parts of the nasopharynx were found to have minimum intraluminal pressure in the airway during inhalation. During exhalation, the pressures in the soft palate were higher compared to those in the larynx, epiglottis, and nasopharynx. Turbulent kinetic energy was found to be maximum at the entry to the airway and gradually decreased as the flow moved inside the airway. However, turbulent kinetic energy increased in regions of the airway with abrupt change in area. Based on the analysis of pressure distribution at different sections of the airway, it was concluded that the front part of the nasopharynx requires maximum muscular activity to support it during inhalation. During exhalation, the soft palate is susceptible to displacements due to presence of high pressures. These can serve as critical information for diagnosis and treatment planning of diseases known to affect the soft palate and nasopharynx in horses, and can potentially be useful for human beings.