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PRP and Prolotherapy are individual treatments that were even suggested and used in combination with stem cell therapies. Prolotherapy predates PRP as a chemical irritant therapy originally used to sclerose tissues. Prolotherapy is meant to stimulate platelet derived growth factors release to improve tissue healing response. Prolotherapy shows negligible efficacy improvements over corticosteroids, but may have underlying side effects from being an irritant. PRP is a more modern therapy for improved healing. Speculations state initial use was in an open heart surgery to improve healing post-surgery. PRP is created via centrifugation of patient blood to isolate growth factors by removing serum and other biological components to increase platelet concentration. PRP is comparable to corticosteroid injections in efficacy, but as an autologous application, there are no side effects making it more advantageous. Growth factors induce healing response and reduce inflammation. Growth factors stimulate cell growth, proliferation, differentiation, and stimulate cellular response mechanism such as angiogenesis and mitogenesis. The growth factor stimulation of PRP and prolotherapy both assist stem cell proliferation. Additional research is needed to determine differential capacity to ensure multipotent stem cells regenerate the correct cell type from the increased differential capacity offered by growth factor recruitment. The application of combination therapy for stem cells is unsubstantiated and applications violate FDA ‘minimal manipulation’ guidelines.
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Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
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