Small volume bone marrow aspirates with high progenitor cell concentrations maximize cell therapy dose manufacture and substantially reduce donor hemoglobin loss.

BACKGROUND: Bone marrow (BM) transplantation is a life-saving therapy for hematological diseases, and the BM harbors also highly useful (progenitor) cell types for novel cell therapies manufacture. Yet, the BM collection technique is not standardized. METHODS: Benchmarking our collection efficiency to BM collections worldwide (N = 1248), we noted a great variability of total nucleated cell (TNC) yields in BM products (HPC-M) with superior performance of our center, where we have implemented a small volume aspirate policy. Thus, we next prospectively aimed to assess the impact of BM collection technique on HPC-M quality. For each BM collection (N = 20 donors), small volume (3 mL) and large volume (10 mL) BM aspirates were sampled at 3 time points and analyzed for cell composition. RESULTS: Compared to large volume aspirates, small volume aspirates concentrated more TNCs, immune cells, platelets, hematopoietic stem/progenitor cells, mesenchymal stromal cells (MSCs), and endothelial progenitors. Inversely, the hemoglobin concentration was higher in large volume aspirates indicating more hemoglobin loss. Manufacturing and dosing scenarios showed that small volume aspirates save up to 42% BM volume and 44% hemoglobin for HPC-M donors. Moreover, MSC production efficiency can be increased by more than 150%. CONCLUSIONS: We propose to consider small volume BM aspiration as standard technique for BM collection.

From Unit to Dose: A Machine Learning Approach for Precise Prediction of Hemoglobin and Iron Content in Individual Packed Red Blood Cell Units.

Transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits survival of chronically transfused patients. Quality control methods, which involve entering pRBC units and removing them from the blood supply, reveal that hemoglobin (38.5-79.9 g) and heme iron (133.42-276.89 mg) vary substantially between pRBCs. Yet, neither hemoglobin nor iron content can be quantified for individual clinically used pRBCs leading to rules of thumb for pRBC transfusions. Keeping their integrity, the authors seek to predict hemoglobin/iron content of any given pRBC unit applying eight machine learning models on 6,058 pRBCs. Based on thirteen features routinely collected during blood donation, production and quality control testing, the model with best trade-off between performance and complexity in hemoglobin/iron content prediction is identified. Validation of this model in an independent cohort of 2637 pRBCs confirms an adjusted R2 > 0.9 corresponding to a mean absolute prediction error of ≤1.43 g hemoglobin/4.96 mg iron (associated standard deviation: ≤1.13 g hemoglobin/3.92 mg iron). Such unprecedented precise prediction enables reliable pRBC dosing per pharmaceutically active agent, and monitoring iron uptake in patients and individual iron loss in donors. The model is implemented in a free open source web application to facilitate clinical application.

[Fatigue syndromes--an overview of terminology, definitions and classificatory concepts]. / Erschöpfungssyndrome--Eine Diskussion verschiedener Begriffe, Definitionsansätze und klassifikatorischer Konzepte.

This article aims at giving a general view of fatigue syndromes, their description, and their differentiation. The syndromes neurasthenia, chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), and burnout are discussed. First, the historical background of fatigue classification is shortly reviewed. Each syndrome is introduced in terms of definition and classification as well as differentiation from each other. The article discusses the differentiation of the syndromes from each other as well as differentiation of CFS/ME and burnout from depression. We conclude that it is difficult to differentiate criteria due to insufficient empirical evidence. More research is needed concerning integration of the diagnoses in classification systems as well as differentiation between syndromes. High comorbidity of depression with CFS and Burnout can be shown, but diagnoses also comprise distinct symptoms.