Objectives: COVID-19 is an ongoing global pandemic. There is an urgent need for identification and understanding of clinical and laboratory parameters related to progression towards a severe and fatal form of this illness, often preceded by a so-called cytokine-storm syndrome (CSS). Therefore, we explored the hemocytometric characteristics of COVID-19 patients in relation to the deteriorating clinical condition CSS, using the Sysmex XN-10 hematology analyzer.Methods: From March 1st till May 16th, 2020, all patients admitted to our hospital with respiratory complaints and suspected for COVID-19 were included (n=1,140 of whom n=533 COVID-19 positive). The hemocytometric parameters of immunocompetent cells in peripheral blood (neutrophils [NE], lymphocytes [LY] and monocytes [MO]) obtained upon admission to the emergency department (ED) of COVID-19 positive patients were compared with those of the COVID-19 negative ones. Moreover, patients with CSS (n=169) were compared with COVID-19 positive patients without CSS, as well as with COVID-19 negative ones.Results: In addition to a significant reduction in leukocytes, thrombocytes and absolute neutrophils, it appeared that lymphocytes-forward scatter (LY-FSC), and reactive lymphocytes (RE-LYMPHO)/leukocytes were higher in COVID-19-positive than negative patients. At the moment of presentation, COVID-19 positive patients with CSS had different neutrophils-side fluorescence (NE-SFL), neutrophils-forward scatter (NE-FSC), LY-FSC, RE-LYMPHO/lymphocytes, antibody-synthesizing (AS)-LYMPHOs, high fluorescence lymphocytes (HFLC), MO-SSC, MO-SFL, and Reactive (RE)-MONOs. Finally, absolute eosinophils, basophils, lymphocytes, monocytes and MO-FSC were lower in patients with CSS.Conclusions: Hemocytometric parameters indicative of changes in immunocompetent peripheral blood cells and measured at admission to the ED were associated with COVID-19 with and without CSS.

Acute myeloid leukemia (AML) is a myeloid malignancy that is characterized by the accumulation of leukemic blast cells, which originate from hematopoietic stem cells that have undergone leukemic transformation and/or are more mature progenitors that have gained stemness features. Currently, no consensus exists for the flow cytometric identification of normal blast cells and their leukemic counterparts by their antigenic expression profile. Differentiating between the benign cells and the malignant cells is crucial for the further deployment of immunophenotype panels for the clinical follow-up of AML patients. This review provides an overview of immunophenotypic markers that allow the identification of leukemic blast cells in the bone marrow with multiparameter flow cytometry. This technique allows the identification of hematopoietic blast cells at the level of maturing cells by their antigen expression profile. While aberrant antigen expression of a single immunophenotypic marker cell cannot be utilized in order to differentiate leukemic blast cells from normal blast cells, combinations of multiple immunophenotypic markers can enable the distinction of normal and leukemic blast cells. The identification of these markers has provided new perspectives for tailored clinical follow-up, including therapy management, diagnostics, and prognostic purposes. The immunophenotypic marker panels, however, should be developed by carefully considering the variable antigen marker expression profile of individual patients.

Blood cell morphology assessment via light microscopy constitutes a cornerstone of haematological diagnostics, providing crucial insights into diverse pathological conditions. This complex task demands expert interpretation owing to subtle morphological variations, biological heterogeneity and technical imaging factors that obstruct automated approaches. Conventional machine learning methods using discriminative models struggle with domain shifts, intraclass variability and rare morphological variants, constraining their clinical utility. We introduce CytoDiffusion, a diffusion-based generative classifier that faithfully models the distribution of blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, data efficiency and uncertainty quantification that surpasses clinical experts. Our approach outperforms state-of-the-art discriminative models in anomaly detection (area under the curve, 0.990 versus 0.916), resistance to domain shifts (0.854 versus 0.738 accuracy) and performance in low-data regimes (0.962 versus 0.924 balanced accuracy). In particular, CytoDiffusion generates synthetic blood cell images that expert haematologists cannot distinguish from real ones (accuracy, 0.523; 95% confidence interval: [0.505, 0.542]), demonstrating good command of the underlying distribution. Furthermore, we enhance model explainability through directly interpretable counterfactual heat maps. Our comprehensive evaluation framework establishes a multidimensional benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings.

Although flow cytometric detection of myelodysplastic syndrome (MDS) with the Ogata score has a high specificity, its sensitivity for low-grade MDS is low. Additional markers are needed to improve its diagnostic reliability. Therefore, we investigated the diagnostic performance of the Ki-67 proliferation index in bone marrow (BM) cell populations for detection of MDS. BM aspirates from 50 MDS patients and 20 non-clonal cytopenic controls were analyzed with flow cytometry to determine the Ogata score and the Ki-67 proliferation indices in different cell populations. Ki-67 proliferation indices alone could be used to detect MDS with a sensitivity of up to 80 % and specificity of up to 70 %. Combining the Ogata score with the Ki-67 proliferation index of erythroid cells significantly improved its sensitivity for detection of MDS from 66 % to 90 %, while maintaining a specificity of 100 %. Particularly, the sensitivity for detection of low-grade MDS improved from 56 % to 91 %. This is the first study using Ki-67 proliferation indices to detect MDS and shows their particularly high diagnostic sensitivity for detection of low-grade MDS. Integration of the Ki-67 proliferation index of erythroid cells into the Ogata score significantly improved its sensitivity without loss of the high specificity.