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This technical paper introduces a novel organ preservation system based on isochoric (constant volume) supercooling. The system is designed to enhance the stability of the metastable supercooling state, offering potential long-term preservation of large biological organs at subfreezing temperatures without the need for cryoprotectant additives. Detailed technical designs and usage protocols are provided for researchers interested in exploring this field. The paper also presents a control system based on the thermodynamics of isochoric freezing, utilizing pressure monitoring for process control. Sham experiments were performed using whole pig liver sourced from a local food supplier to evaluate the system’s ability to sustain supercooling without ice nucleation for extended periods. The results demonstrated sustained supercooling without ice nucleation in pig liver tissue for 24 and 48 h. These findings suggest the potential of this technology for large-volume, cryoprotectant-free organ preservation with real-time control over the preservation process. The simplicity of the isochoric supercooling device and the design details provided in the paper are expected to serve as encouragement for other researchers in the field to pursue further research on isochoric supercooling. However, final evidence that these preserved organs can be successfully transplanted is still lacking. 

Abstract Human induced pluripotent stem cell (iPSC)-derived liver organoids serve as models of organogenesis, disease, drug screening, and regenerative medicine. Prevailing methods for generating organoids rely on Matrigel, whose batch-to-batch variability and xenogeneic source pose challenges to mechanistic research and translation to human clinical therapy. In this report, we demonstrate that self-assembled Matrigel-free iPSC-derived organoids developed in rotating wall vessels (RWVs) exhibit greater hepatocyte-specific functions than organoids formed on Matrigel. We show that RWVs produce highly functional liver organoids in part by eliminating the need for Matrigel, which has adverse effects on hepatic lineage differentiation. RWV liver organoids sustain durable function over long-term culture and express a range of mature functional genes at levels comparable to adult human liver, while retaining some fetal features. Our results indicate that RWVs provide a simple and high-throughput way to generate Matrigel-free liver organoids suitable for research and clinical applications. 

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.