Unexpected hope for a multiple myeloma patient

Multiple myeloma (MM) is a plasma cell neoplasm, characterized by periods of remission and relapses. The emergence of novel therapies, with multiple mechanisms of action and fewer adverse reactions, brings more and better options and also a higher survival rate. However, MM is still an incurable disease, and patients eventually become refractory to an extensive range of therapies. We present the case of a patient diagnosed with MM standard risk, who was at first refractory to multiple treatment regimens, and then had an unexpected and stable complete response to a newer drug of the same class

REGULATORY T CELLS AND THE MICROENVIRONMENT OF THE MALIGNANT B CELL OF CHRONIC LYMPHOCYTIC LEUKEMIA

In recent years understanding and modulating the tumor microenvironment (MT) has been the focus of a scientifically and clinically intense study. The role of T regulatory cells (Tregs) were investigated in terms of the suppression of tumor-specific immune responses and the establishment of an immunosuppressive tumor microenvironment (1). Regulatory T cells have a fundamental function in maintaining immune homeostasis in healthy individuals, and in cancer and in particular in haematological malignancies they seem to play a rather controversial role. Furthermore an increased frequency of Treg cells has been associated with tumor progression and has been correlated with an increased risk of death and reduced survival (2). The role of T cells in the pathogenesis of chronic lymphocytic leukemia has recently gained special attention due to the constant interaction between neoplastic B cells with the micromedium substrate and T cells. There is often a relatively large number of regulatory T cells in lymphoid tissues of CLL patients, that could affect the normal immune function (3)

Current Understanding of Immune Thrombocytopenia: A Review of Pathogenesis and Treatment Options

The management of immune thrombocytopenia (ITP) and the prediction of patient response to therapy still represent a significant and constant challenge in hematology. ITP is a heterogeneous disease with an unpredictable evolution. Although the pathogenesis of ITP is currently better known and its etiology has been extensively studied, up to 75% of adult patients with ITP may develop chronicity, which represents a significant burden on patients’ quality of life. A major risk of ITP is bleeding, but knowledge on the exact relationship between the degree of thrombocytopenia and bleeding symptoms, especially at a lower platelet count, is lacking. The actual management of ITP is based on immune suppression (corticosteroids and intravenous immunoglobulins), or the use of thrombopoietin receptor agonists (TPO-RAs), rituximab, or spleen tyrosine kinase (Syk) inhibitors. A better understanding of the underlying pathology has facilitated the development of a number of new targeted therapies (Bruton’s tyrosine kinase inhibitors, neonatal Fc receptors, strategies targeting B and plasma cells, strategies targeting T cells, complement inhibitors, and newer TPO-RAs for improving megakaryopoiesis), which seem to be highly effective and well tolerated and result in a significant improvement in patients’ quality of life. The disadvantage is that there is a lack of knowledge of the predictive factors of response to treatments, which would help in the development of an optimized treatment algorithm for selected patients

Idiopathic thrombocytopenic purpura (ITP) – new era for an old disease

Immune thrombocytopenia is an autoimmune hematological disorder characterized by severely decreased platelet count of peripheral cause: platelet destruction via antiplatelet antibodies which may also affect marrow megakaryocytes. Patients may present in critical situations, with cutaneous and/or mucous bleeding and possibly life-threatening organ hemorrhages (cerebral, digestive, etc.) Therefore, rapid diagnosis and therapeutic intervention are mandatory

Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data

This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware