Management of patients with lower-risk myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic stem cell disorders characterized by ineffective hematopoiesis with abnormal blood cell development (dysplasia) leading to cytopenias and an increased risk for progression to acute myeloid leukemia (AML). Patients with MDS can generally be classified as lower- (LR-MDS) or higher-risk (HR-MDS). As treatment goals for patients with LR-MDS and those with HR-MDS differ significantly, appropriate diagnosis, classification, and follow-up are critical for correct disease management. In this review, we focus on the diagnosis, prognosis, and treatment options, as well as the prediction of the disease course and monitoring of treatment response in patients with LR-MDS. We discuss how next-generation sequencing, increasing knowledge on mechanisms of MDS pathogenesis, and novel therapies may change the current treatment landscape in LR-MDS and why structured assessments of responses, toxicities, and patient-reported outcomes should be incorporated into routine clinical practice

Tumor immune escape in acute myeloid leukemia: Class II-associated invariant chain peptide expression as result of deficient antigen presentation

In this overview, we discuss the role of class II-associated invariant chain peptide (CLIP) in acute myeloid leukemia (AML), one of the few tumors expressing HLA class II. The clinical impact, function and regulation of CLIP expression on leukemic cells is addressed, indicating its potential as immunotherapeutic target in AML

Nieuwste ontwikkelingen met betrekking tot biologische defosfatering

Uit het onderzoek aan de Landbouwhogeschool komt naar voren, dat biologische defosfatering ook bij lage slibbelastingen tot reele mogelijkheden behoort. Toevoeging van azijnzuur aan het influent garandeert een goed werkend en stabiel systeem. Rendementen van fosfaatvewijdering tot boven 90 procent zijn haalbaa

Different carbon isotope fractionation patterns during the development of phototrophic freshwater and marine biofilms

Natural phototrophic biofilms are influenced by a broad array of abiotic and biotic factors and vary over temporal and spatial scales. Different developmental stages can be distinguished and growth rates will vary due to the thickening of the biofilm, which is expected to lead to a limitation of light or mass transport. This study shows that variation in CO<sub>2(aq)</sub> availability leads to a fractionation shift and thereby affects δ<sup>13</sup>C signatures during biofilm development. For phototrophic freshwater biofilms it was found that the δ<sup>13</sup>C value became less negative with the thickening of the biofilm, while the opposite trend was found in marine biofilms. Modeling and pH profiling indicated that the trend in the freshwater system was caused by an increase in CO<sub>2(aq)</sub> limitation resulting in an increase of HCO<sub>3</sub><sup>−</sup> as C-source. The opposite trend in the marine system could be explained by a higher heterotrophic biomass and activity causing a higher carbon recycling and thereby lower δ<sup>13</sup>C values. We conclude that δ<sup>13</sup>C was more related to the net areal photosynthesis rate and carbon recycling, rather than to the growth rate of the biofilms

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient)