23 research outputs found
Is the Use of 9/10 HLA Unrelated Donors Still Acceptable in Allogeneic Hematopoietic Stem Cell Transplantation for Hematological Malignancies? Comparison with Transplants from 10/10 HLA Unrelated Donors and Siblings
Faster Registration on International Donor Registries and Shorter Time to Allogeneic Hematopoietic Stem Cell Transplantation After Having Found a Donor Confers Better Outcome in Acute Leukemia Patients
Highly Significant Impact of HLA-DRB3 and -DRB4 Matching on Different Unrelated allo-HSCT Outcomes: New Perspectives in the Unrelated Donor Selection
Adsorption dâun tensio-actif cationique sur Ă©lectrode Ă mercure
L'objectif de ce travail est d'Ă©tudier l'adsorption d'un tensioactif cationique le DTAB (dodĂ©cyltrimĂ©thylammonium bromure), en prĂ©sence d'un sel NaBr, en utilisant comme surface modĂšle l'Ă©lectrode Ă mercure. Les essais ont montrĂ© que ce cationique forme une bi- couche. Ce rĂ©sultat diffĂšre de celui trouvĂ© antĂ©rieurement avec un dĂ©tergent anionique de mĂȘme longueur de chaĂźne aliphatique, le SDS ou dodĂ©cylsulfate de sodium en prĂ©sence de NaCl, mais semblable Ă celui des dĂ©tergents cationiques le CTAC (cĂ©tyltrimĂ©thyl ammonium chlorure) ou le CTAB (cĂ©tyltrimĂ©thyl-ammonium bromure) en prĂ©sence respectivement de KCl et KBr. Les courbes isopotentielles ont permis de dĂ©limiter les valeurs de la CMC (concentration micellaire critique). L'addition de sels diminue la CMC. Il apparaĂźt que pour le DTAB, ces variations sont liĂ©es non seulement Ă un effet de couche diffuse mais aussi Ă un effet de relargage
A Transfer Learning Approach to Classify the Brain Age from MRI Images
Predicting brain age from Magnetic Resonance Imaging (MRI) can be used to identify neurological disorders at an early stage. The brain contour is a biomarker for the onset of brain-related problems. Artificial Intelligence (AI) based Convolutional Neural Networks (CNN) is used to detect brain related problems in MRI images. However, conventional CNN is a complex architecture and the time to process the image, large data requirement and overfitting are some of its challenges. This study proposes a transfer learning approach using InceptionV3 to classify brain age from the MRI images in order to improve the brain age classification model. Models are trained on an augmented OASIS (Open Access Series of Imaging Studies) dataset which contains 411 raw and 411 masked MRI images of different people. The models are evaluated using testing accuracy, precision, recall, and F1 Scores. Results demonstrate that InceptionV3 has a testing accuracy of 85%. This result demonstrates the potential for InceptionV3 to be used by medical practitioners to detect brain age and the potential onset of neurological disorders from MRI images
Allogeneic bone marrow tranplantation for young adult patients with newly diagnosed acute myeloid leukemia: HLA-Matched sibling donor availability does not improve treatment ouctome
Formation of C-S-H in calcium hydroxideâblast furnace slagâquartzâwater system in autoclaving conditions
Automatic depletion with Spectra Optia allows a safe 16% reduction of red blood cell pack consumption in exchanged sickle cell anemia patients
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