Description of lymphocyte and cytokine profiles in individuals with acute myeloid leukemia associated with FLT3-ITD and NPM1 mutation status.

The pathogenesis of acute myeloid leukemia (AML) involves mutations in genes such as FLT3 and NPM1, which are also associated with the prognosis of the disease. The immune system influences disease progression, but the mechanisms underlying the interaction between the immune system and AML are not clear. In this study, the profiles of lymphocytes and cytokines were described in individuals with AML stratified by molecular changes associated with prognosis. The participants included in this study were newly diagnosed AML patients (n = 43) who were about to undergo chemotherapy. Subtypes of lymphocytes in peripheral blood, including B cells, T cells, and natural killer cells, and serum concentrations of cytokines, including Th1, Th2, and Th17, were studied by flow cytometry assays (BD FACSCanto II). The correlations between lymphocyte subsets, cytokines, and genetic/prognostic risk stratification (based on the FLT3 and NPM1 genes) were analyzed. The differences in B lymphocytes (%), T lymphocytes (%), plasmablasts (%), leukocytes (cells/µl), and tumor necrosis factor (pg/ml) were determined between groups with FLT3-ITD+ and FLT3-ITD- mutations. The presence of mutations in NPM1 and FLT3-ITD and age suggested changes in the lymphocyte and cytokine profile in individuals with AML.

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists.

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. Diagnosing MM presents considerable challenges, involving the identification of plasma cells in cytology examinations on hematological slides. At present, this is still a time-consuming manual task and has high labor costs. These challenges have adverse implications, which rely heavily on medical professionals' expertise and experience. To tackle these challenges, we present an investigation using Artificial Intelligence, specifically a Machine Learning analysis of hematological slides with a Deep Neural Network (DNN), to support specialists during the process of diagnosing MM. In this sense, the contribution of this study is twofold: in addition to the trained model to diagnose MM, we also make available to the community a fully-curated hematological slide dataset with thousands of images of plasma cells. Taken together, the setup we established here is a framework that researchers and hospitals with limited resources can promptly use. Our contributions provide practical results that have been directly applied in the public health system in Brazil. Given the open-source nature of the project, we anticipate it will be used and extended to diagnose other malignancies.