Cellular therapies are living drugs whose efficacy depends on persistence and survival. Expansion of therapeutic T cells employs hypermetabolic culture conditions to promote T cell expansion. We show that typical in vitro expansion conditions generate metabolically and functionally impaired T cells more reliant on aerobic glycolysis than those expanding in vivo.
View Article and Find Full Text PDFMolecular assessment of measurable residual disease (MRD) in NPM1-mutated AML patients is a powerful prognostic tool to identify the risk of relapse. There is limited data regarding MRD-guided decisions against alloSCT in elderly patients and FLT3-ITD co-mutation. We describe the outcome of NPM1-mutated AML patients in whom alloSCT was deferred based on ELN 2017 risk and MRD response.
View Article and Find Full Text PDFBackground: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance.
Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate.
This pilot study evaluated CPX-351 in adults with newly diagnosed, favourable-intermediate risk, FLT3-ITD-negative AML. Twenty patients received CPX-351 for induction, with six also receiving gemtuzumab ozogamicin (GO). The complete response rate was 95%, with 42% achieving flow-based minimal residual disease (MRD) negativity post-induction.
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