Presentation number: MG 50
SYSTEMS APPROACH TO HEMOANALYTIC CHARACTERIZATION OF PLATELET RICH PLASMA AND BONE MARROW CONCENTRATE AND AI ALGORITHMIC DATA ANALYSIS OF DOSE-RESPONSE RELATIONSHIPS
David Karli1, Richard Bedford2, Joe Reyes2, Adis Kljajic3 Dragan Primorac4-14
1Steadman Clinic, Vail, CO USA, 2Greyledge Technologies Lone Tree, CO USA, 3ONQODE, Sterling Heights, Michigan, USA, 4St. Catherine Specialty Hospital, Zagreb, Croatia, 5Medical School, University of Zagreb, Zagreb, Croatia, 6University of Osijek Faculty of Dental Medicine & Health, Osijek, Croatia, 7University of Split School of Medicine, đ Split, Croatia , 8Department of Biochemistry & Molecular Biology, The Pennsylvania State University, State College, PA, USA, 9The Henry C Lee College of Criminal Justice & Forensic Sciences, University of New Haven, West Haven, CT, USA, 10The National Forensic Sciences University, Gandhinagar, Gujarat, India, 11University of Rijeka, School of Medicine, Rijeka, Croatia, 12Medical School REGIOMED, Coburg, Germany, 13School of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia, 14Medical School, University of Mostar, Mostar, Bosnia and Herzegovina
Autologous cell therapies, including Platelet Rich Plasma (PRP) and Bone Marrow Concentrate (BMC), continue to be limited by a lack of quality control. Existing commercial systems lack the ability to standardize or quantify cellular concentrations within these preparations. As a result, most research to date lacks any specific analysis of cell or platelet dose-response relationships defining what constitutes an optimized product. Presented is a systems approach to address quality deficiencies in autologous point of care cellular products (Greyledge Technologies USA). Acquired samples from peripheral and medullary blood respectively, along with manually processed preparations of PRP and BMC are quantified using a validated hemoanalyzer (Sysmex XN-350). Analyses include WBC (TNCC) with 6-part differential (Lymphocyte, Monocyte, Neutrophil, Basophil, Eosinophil and Immature Granulocyte), RBC and Platelet count. Sample analytics are imported into a cloud-based data platform (Greyledge Cloud) along with patient demographic information and body-part specific patient reported outcomes questionnaires. Outcomes are collected pre-treatment and at 1,3,6,12 and 18month intervals. Greyledge uses a secure HIPAA certified database along with the most advanced programming languages to digitally combine data sets which are transmitted into AI/ML algorithms. A CSV Document is exported that dynamically combines tailored cellular data sets which are examined using Python as the backend framework in combination with Rcode, Node and Angular JS libraries to perform dynamic artificial intelligence studies. At present the file contains 7,000 discreet variables attributable to 200 patients (4000 sample analytic data points and 3000 demographic data points with an average of 35 variables/patient). Cellular dose-response trends and relationships are under investigation to effectively develop predictive models of success and failure, along with optimal product parameters specific to patient demographics and treatment indication.
Key words: bone marrow-derived stem cells, platelet rich plasma, regenerative medicine, PRP, mesenchymal stem cell