With advances in conditioning strategies and graft-versus-host disease (GvHD) prevention, hematopoietic stem cell transplantation (HSCT) is a safe, curative treatment option for pediatric patients with sickle cell disease (SCD). However, donor options have been limited in non-myeloablative matched sibling donor (MSD) setting by excluding recipients with major ABO blood group incompatible donors due to concern of the risk of significant complications such as pure red cell aplasia (PRCA). We present three cases of successful HSCT with major ABO incompatibility with their donors, and discuss strategies to safely expand the donor pool to include these donors.
View Article and Find Full Text PDFBackground: Allogeneic transplant for patients with transfusion-dependent thalassemia is challenging once there has been iron overload and chronic transfusion support.
Objective(s): A transplant strategy that reduced intensity of the preparative regimen and tailored immunosuppression to both support donor engraftment and prevent GVHD was developed for this population. The combination of a pretransplant immunosuppression phase with reduced dosing of fludarabine/prednisone, treosulfan-based preparative regimen with reduced cyclophosphamide dosing, and introduction of a calcineurin/methotrexate-free GVHD prophylaxis/engraftment supporting regimen with abatacept/sirolimus/ATG was tested.
Aggregative multicellularity is a cooperative strategy employed by some microorganisms. Unlike clonal expansion within protected environments during multicellular eukaryotic development, an aggregation strategy introduces the potential for genetic conflicts and exploitation by cheaters, threatening the stability of the social system. , a soil-dwelling bacterium, employs aggregative multicellularity to form multicellular fruiting bodies that produce spores in response to starvation.
View Article and Find Full Text PDFEarly identification and intervention often leads to improved life outcomes for individuals with Autism Spectrum Disorder (ASD). However, traditional diagnostic methods are time-consuming, frequently delaying treatment. This study examines the application of machine learning (ML) techniques to 10-question Quantitative Checklist for Autism in Toddlers (QCHAT-10) datasets, aiming to evaluate the predictive value of questionnaire features and overall accuracy metrics across different cultures.
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