SummaryDespite sickle cell disease (SCD) being the most common and severe inherited condition worldwide, therapeutic options are limited. To date, hydroxyurea remains the main treatment option in SCD. However, in the last decade the numbers of interventional clinical trials focussing on therapies for SCD have increased significantly. Many new drugs with various pharmacological targets have emerged and, although the majority have failed to show benefit in clinical trials, some have produced encouraging results. It seems probable that more drugs will soon become available for the treatment of SCD. Furthermore, promising clinical trials with improved outcomes have recently changed the perspective of curative therapies in SCD. Nevertheless, the application of novel therapeutic agents and potential curative treatments will most likely be limited to high‐income countries and, thus, will remain unavailable for the majority of people with SCD in the foreseeable future.
The loss of red blood cell (RBC) deformability in sickle cell anaemia (SCA) is considered the primary factor responsible for episodes of acute pain and downstream progressive organ dysfunction. Oxygen gradient ektacytometry (Oxygenscan) is a recently commercialised functional assay that aims to describe the deformability of RBCs in SCA at differing oxygen tensions. So far, the Oxygenscan has been evaluated only by a small number of research groups and the validity and clinical value of Oxygenscan-derived biomarkers have not yet been fully established. In this study we examined RBC deformability measured with the Oxygenscan in 91 children with SCA at King's College Hospital in London. We found a significant correlation between Oxygenscan-derived biomarkers and well-recognised modifiers of disease severity in SCA: haemoglobin F and co-inherited a-thalassaemia. We failed, however, to find any independent predictive value of the Oxygenscan in the clinical outcome measure of pain, as well as other important parameters such as hydroxycarbamide treatment. Although the Oxygenscan remains an intriguing tool for basic research, our results question whether it provides any additional information in predicting the clinical course in children with SCA, beyond measuring known markers of disease severity.
The spleen plays an important role in the body’s defence against bacterial infections. Measuring splenic function is of interest in multiple conditions, including sickle cell anaemia (SCA), where spleen injury occurs early in life. Unfortunately, there is no direct and simple way of measuring splenic function, and it is rarely assessed in clinical or research settings. Manual counts of pitted red blood cells (RBCs) observed with differential interference contrast (DIC) microscopy is a well-validated surrogate biomarker of splenic function. The method, however, is both user-dependent and laborious. In this study, we propose a new automated workflow for counting pitted RBCs using deep neural network analysis. Secondly, we assess the durability of fixed RBCs for pitted RBC counts over time. We included samples from 48 children with SCA and 10 healthy controls. Cells were fixed in paraformaldehyde and examined using an oil-immersion objective, and microscopy images were recorded with a DIC setup. Manual pitted RBC counts were performed by examining a minimum of 500 RBCs for pits, expressing the proportion of pitted RBCs as a percentage (%PIT). Automated pitted RBC counts were generated by first segmenting DIC images using a Zeiss Intellesis deep learning model, recognising and segmenting cells and pits from background. Subsequently, segmented images were analysed using a small ImageJ macro language script. Selected samples were stored for 24 months, and manual pitted RBC counts performed at various time points. When comparing manual and automated pitted RBC counts, we found the two methods to yield comparable results. Although variability between the measurements increased with higher %PIT, this did not change the diagnosis of asplenia. Furthermore, we found no significant changes in %PIT after storing samples for up to 24 months and under varying temperatures and light exposures. We have shown that automated pitted RBC counts, produced using deep neural network analysis, are comparable to manual counts, and that fixed samples can be stored for long periods of time without affecting the %PIT. Automating pitted RBC counts makes the method less time consuming and results comparable across laboratories.
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