Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction model for a computer numerical control (CNC) milling machine tool. Energy prediction models for different machining operations are constructed based on collected data. With the collected data sets, optimum input features for model selection are identified. We demonstrate how the energy prediction models can be used to compare the energy consumption for the different operations and to estimate the total energy usage for machining a generic part. We also present an uncertainty analysis to develop confidence bounds for the prediction model and to provide insight into the vast parameter space and training required to improve the accuracy of the model. Generic parts are machined to test and validate the prediction model constructed using the Gaussian Process and we consistently achieve an accuracy of over 95 % on the total predicted energy.

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Mesenchymal stem cell (MSC) therapy has been widely tested in clinical trials to promote healing post-myocardial infarction. However, low cell retention and the need for a large donor cell number in human studies remain a key challenge for clinical translation. Natural biomaterials such as gelatin are ideally suited as scaffolds to deliver and enhance cell engraftment after transplantation. A potential drawback of MSC encapsulation in the hydrogel is that the bulky matrix may limit their biological function and interaction with the surrounding tissue microenvironment that conveys important injury signals. To overcome this limitation, we adopted a gelatin methacrylate (gelMA) cell-coating technique that photocross-links gelatin on the individual cell surface at the nanoscale. The present study investigated the cardiac protection of gelMA coated, hypoxia preconditioned MSCs (gelMA-MSCs) in a murine myocardial infarction (MI) model. We demonstrate that the direct injection of gelMA-MSC results in significantly higher myocardial engraftment 7 days after MI compared to uncoated MSCs. GelMA-MSC further amplified MSC benefits resulting in enhanced cardioprotection as measured by cardiac function, scar size, and angiogenesis. Improved MSC cardiac retention also led to a greater cardiac immunomodulatory function after injury. Taken together, this study demonstrated the efficacy of gelMA-MSCs in treating cardiac injury with a promising potential to reduce the need for donor MSCs through enhanced myocardial engraftment.

As the fields of aging and neurological disease expand to liquid biopsies, there is a need to identify informative biomarkers for the diagnosis of neurodegeneration and other age-related disorders such as cancers. A means of high-throughput screening of biomolecules relevant to aging can facilitate this discovery in complex biofluids, such as blood. Exosomes, the smallest of extracellular vesicles, are found in many biofluids and, in recent years, have been found to be excellent candidates as liquid biopsy biomarkers due to their participation in intercellular communication and various pathologies such as cancer metastasis. Recently, exosomes have emerged as novel biomarkers for age-related diseases. Hence, the study of exosomes, their protein and genetic cargo can serve as early biomarkers for age-associated pathologies, especially neurodegenerative diseases. However, a disadvantage of exosome studies includes a lack in standardization of isolating, detecting, and profiling exosomes for downstream analysis. In this review, we will address current techniques for high-throughput isolation and detection of exosomes through various microfluidic and biosensing strategies and how they may be adapted for the detection of biomarkers of age-associated disorders.