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Microfluidic biotechnologies for hematology: separation, disease detection and diagnosis
Published in Annie Viallat, Manouk Abkarian, Dynamics of Blood Cell Suspensions in Microflows, 2019
Platelets are the smallest cellular component in blood. They play an important role in stopping bleeding and enabling wound sealing. Separation of viable platelets is crucial for platelet transfusion in treatment of patients with acute bleeding or clotting malfunction. Plateletpheresis is widely adopted in clinics to extract only platelets from donor's whole blood and circulate the other blood components back to the donor. This process usually takes around an hour to collect enough platelets for one unit (around 3 1011) of platelet transfusion. However, in physiological research and clinical diagnosis, fast separation of platelets for downstream analysis is always desired. Additionally, the centrifuge based plateletpheresis has been shown to cause damage or activation of platelets which is undesirable for their downstream application [5]. Several microfluidic systems have also been developed to perform on-chip plateletpheresis. Due to the significantly smaller size of platelets as compared to other blood cells, the separation efficiency is usually high and the separation process is relatively simpler as compared to the separation of other blood cells.
Transfusion of blood components in a stem cell transplant programme
Published in Cut Adeya Adella, Stem Cell Oncology, 2018
As described, the results of a transfusion of a unit of platelets can vary based on compatibility issues between donor and patient. Therefore, the effects of a platelet transfusion need to be monitored. The easiest way to do this is a patient platelet count 1 and 24 hours after the transfusion, taking into account the number of platelets in the product and the body surface area of the patient (corrected count index (CCI)). One important issue in refractoriness is the HLA antibodies of the patient. The one-hour CCI will be very low, in other words, there is no increment in the platelet count of the patient. Platelets derived from an HLA compatible donor (taken from a database of HLA typed donors or first and second-degree relatives) can bring a solution. Gamma irradiation of these platelets prior to the transfusion is a requirement.
Hieh-Dose Immunosuppressive Chemotherapy with Autologous Stem Cell Support for Chronic Autoimmune Thrombocytopenia
Published in Richard K. Burt, Alberto M. Marmont, Stem Cell Therapy for Autoimmune Disease, 2019
Richard D. Huhn, Patrick F. Fogarty, Ryotaro Nakamura, Cynthia E. Dunbar
Patients generally tolerated the mobilization and collection procedures well. Side effects of G-CSF were minor, consisting of musculoskeletal discomfort that was transient and treatable with oral analgesics. There was no discernable effect of the G-CSF on platelet counts (such effect would have been difficult to identify in the setting of ongoing autoimmune thrombocytopenia and apheresis procedures). Apheresis catheters were inserted with platelet transfusion support without significant difficulty. Two patients developed femoral hematomas that were managed by administration of local pressure and platelet transfusion. There were no episodes of significant citrate toxicity or other adverse events requiring discontinuation of the apheresis procedures.
Blood transfusion prediction using restricted Boltzmann machines
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
Jenny Cifuentes, Yuanyuan Yao, Min Yan, Bin Zheng
In this way, Walczak (2005) proposes an Artificial Neural Network (ANN) to predict the transfusion requirements of trauma patients using available information, such as age, sex, safety equipment used, Glasgow Coma Scale (GCS), respiratory rate, systolic blood pressure and the number of units of Packed red blood cells (PRBC), Fresh frozen plasma (FFP), and platelets transfused for each patient during the first 2, 6, and 24 h. Accuracy values for the ANN predictions of PRBC, FFP, and platelets range from 67.78% to 91.42%. In a similar approach, Ho and Chang (2011) developed an ANN model in conjunction with the hybrid Taguchi-genetic algorithm (HTGA) to predict the platelet transfusion requirements for the acute myeloblastic leukemia (AML) patients. In this case, the analysis was performed based on demographic factors and clinical features that include the patient’s gender, age, blood type, initial platelet before transfusion, and initial white blood cell (WBC). The validating accuracy of the HTGA-based ANN model to predict the transfusion requirements of the Random Donor Platelet Concentrate (RD-PC) and the Single Donor Platelet Concentrate (SD-PC) on AML patients is 88.64% and 78.10%, respectively.