Publications by authors named "N el Mufti"

Background: Although alloimmunization risk of pathogen-reduced (PR) platelets has been studied, the risk has not been reported with PR red blood cells (RBCs).

Study Design And Methods: In a Phase III, randomized, controlled trial (Red Cell Pathogen Inactivation), cardiac or thoracic-aorta surgery patients were randomized to transfusion with amustaline/glutathione PR versus conventional RBCs. Pre-transfusion and Day 28 samples were evaluated for Human leukocyte antigen (HLA) Class I and Class II antibodies at low, medium, and high cutoff values.

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Background: The clinical significance of natural and treatment-emergent antibodies specific for amustaline/glutathione pathogen-reduced red blood cells (PRRBCs) is not known.

Study Design And Methods: A Phase 3, randomized clinical trial of PRRBCs (ReCePI) compared PRRBCs with conventional RBCs in cardiac or thoracic-aorta surgery. Subjects transfused during and for 7 days after surgery were screened for PRRBC-specific antibodies at baseline, 28 and 75 days post-surgery.

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Acoustic noise can have profound effects on wellbeing, impacting the health of the pregnant mother and the development of the fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels.

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Background: N-(-9 acridinyl)-b-alanine hydrochloride (S-300) is the main byproduct of red blood cell (RBC) amustaline/glutathione(GSH) pathogen reduction, currently undergoing phase III US clinical trials following successful European studies(1-3). Phosphatidylinositol glycan, class A (Pig-a) X-linked gene mutagenesis is a validated mammalian in vivo mutation assay for genotoxicity, assessed as clonal loss of glycosylphosphatidylinositol-linked CD59 cell-surface molecules on reticulocytes (RETs) and RBCs.

Methods: Male Sprague-Dawley rats received continuous infusion of S-300 up to the maximum feasible dose (240 mg/kg/day-limited by solubility and volume) for 28 days.

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Article Synopsis
  • Deep learning models used for looking at medical images can make big mistakes, especially when they see images that are different from what they were trained on.
  • Because of these mistakes, doctors might find it hard to trust these models, so it's really important to have ways to find and fix problems with them.
  • The researchers created a new system that makes sure the AI model is more reliable by checking its predictions against expert knowledge and using a backup method if needed, proving it works well with a large set of brain images from babies.
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