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Allogeneic hematopoietic stem cell transplantation (alloHCT) results in improved outcomes for acute myeloid leukemia (AML) patients compared to consolidation chemotherapy in transplant-eligible patients with adverse-risk disease. Despite achieving a complete remission (CR) to initial therapy, defined as <5% bone marrow blasts with recovery of peripheral blood elements, a large number of patients suffer disease relapse following alloHCT. There is a growing focus on minimal or measurable residual disease (MRD) in patients with AML, variably defined using multiparametric flow cytometry and nucleic acid sequencing techniques, to detect levels of disease not included in current CR terminology. MRD has emerged as an important prognostic tool in AML and may improve risk stratification, further refine selection of post-remission treatment, and provide an opportunity for therapeutic intervention. Herein, we review MRD detection methods, timing of measurement, prognostic implications, and MRD-directed therapy in AML patients undergoing alloHCT. [ABSTRACT FROM AUTHOR]

Both patients developed relapsed disease while receiving calcineurin inhibitors combined with low-dose prednisone (7.5 and 5 mg daily) as GVHD prophylaxis, and neither patient had previously developed GVHD requiring high-dose steroids. Of these 6 patients, 4 remained in CR at day 365 disease assessment, including the patient that had residual CNS disease at time of alloHCT. Aggressive B- and T-cell non-Hodgkin's lymphoma (aNHL) with secondary central nervous system involvement (sCNSL) are rare diseases with poor prognoses. At the time of alloHCT, 1 patient had residual disease in the CNS and the remaining 9 patients were in a complete remission (CR) (6 patients in CR1, 2 patients in CR2, and 1 patient in CR3). [Extracted from the article]

Diffusion coefficients of electrode materials are often determined using galvanostatic (GITT) or potentiostatic intermittent titration technique (PITT), electrochemical impedance spectroscopy (EIS) or cyclic voltammetry (CV). However, these methods require special care as for each, their formal derivations use quite restrictive assumptions. As an alternative, a machine learning model is developed to extend a previously proposed optical method of studying lithium transport by operando microscopy. The herein reported model enables the measurement of concentration-dependent diffusion coefficients. For this purpose, a python code is developed that determines concentration profiles from RGB images using support vector regression (SVR), a flexible machine learning tool. To evaluate the diffusion coefficient, an inverse Boltzmann-Matano concept is applied. Representing the diffusion coefficient with generalized Redlich-Kister polynomials, concentration profiles are predicted and fit to the measured data. The method is demonstrated here on the example of delithiation of LiMn 2 O 4 , but it can, in-principle, be extended to any other battery material showing significant optical response on lithiation, which most of them do. [Display omitted] [ABSTRACT FROM AUTHOR]