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Background Red blood cell (RBC) transfusions are an important treatment modality for patients with sickle cell disease (SCD) and β‐thalassemia. A subgroup of these patients relies on a chronic RBC transfusion regimen. Little is known about RBC survival (RCS) of the transfused allogeneic RBCs. In this study, we aimed to study the RCS kinetics of transfused RBCs in SCD and β‐thalassemia and to investigate factors that determine RCS. Methods and Materials We performed a prospective cohort study on fourteen adults with SCD and β‐thalassemia disease receiving a chronic transfusion regimen. RCS and the influence of donor and patient characteristics on RCS were assessed by simultaneous transfusion of two allogeneic RBCs using RBC biotinylation. Phenotyping of well‐known RBC markers over time was performed using flow cytometry. Results RCS of the two transfused RBC units was similar in most patients. Although intra‐individual variation was small, inter‐individual variation in RCS kinetics was observed. Most patients demonstrated a non‐linear trend in RCS that was different from the observed linear RCS kinetics in healthy volunteers. After an initial slight increase in the proportion of biotinylated RBCs during the first 24 h, a rapid decrease within the first 10–12 days was followed by a slower clearance rate. Conclusion These are the first data to demonstrate that patient‐related factors largely determine post‐transfusion RCS behavior of donor RBC in SCD and β‐thalassemia, while donor factors exert a negligible effect. Further assessment and modeling of RCS kinetics and its determinants in SCD and β‐thalassemia patients may ultimately improve transfusion therapy.

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.