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Osteoarthritis (OA) is a degenerative disease associated with cartilage degradation, osteophyte formation, and fibrillation. Autologous Protein Solution (APS), a type of autologous anti-inflammatory orthobiologic, is used for pain management and treatment of OA. Various compositions of autologous PRP formulations are in clinical use for musculoskeletal pathologies, by nature of their minimal processing and source of bioactive molecules. Currently, there is no consensus on the optimal composition of the complex mixture. In this study, we focused on elucidating the immune cell subtypes and phenotypes in APS. We identified the immune cell types in APS from healthy donors and investigated phenotypic changes in the immune cells after APS processing. Based on flow cytometric analysis, we found that neutrophils and T cells are the most abundant immune cell types in APS, while monocytes experience the largest fold change in concentration compared to WBCs. Gene expression profiling revealed that APS processing results in differential gene expression changes dependent on immune cell type, with the most significantly differentially regulated genes occurring in the monocytes. Our results demonstrate that the mechanical processing of blood, whose main purpose is enrichment and separation, can alter its protein and cellular composition, as well as cellular phenotypes in the final product.

 

Synopsis The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists. 

Synchronization primitives like barriers heavily impact the performance of parallel programs. As core counts increase and granularity decreases, the value of enabling fast barriers increases. Through the evaluation of the performance of a variety of software implementations of barriers, we found the cost of software barriers to be on the order of tens of thousands of cycles on various incarnations of x64 hardware. We argue that reducing the latency of a barrier via hardware support will dramatically improve the performance of existing applications and runtimes, and would enable new execution models, including those which currently do not perform well on multicore machines. To support our argument, we first present the design, implementation, and evaluation of a barrier on the Intel HARP, a prototype that integrates an x64 processor and FPGA in the same package. This effort gives insight into the potential speed and compactness of hardware barriers, and suggests useful improvements to the HARP platform. Next, we turn to the processor itself and describe an x64 ISA extension for barriers, and how it could be implemented in the microarchitecture with minimal collateral changes. This design allows for barriers to be securely managed jointly between the OS and the application. Finally, we speculate on how barrier synchronization might be implemented on future photonics-based hardware.