Gaucher Disease and Myelofibrosis: A Combined Disease or a Misdiagnosis?

Background: Gaucher disease (GD) and primary myelofibrosis (PMF) share similar clinical and laboratory features, such as cytopenia, hepatosplenomegaly, and marrow fibrosis, often resulting in a misdiagnosis. Case Report: We report here the case of a young woman with hepatosplenomegaly, leukopenia, and thrombocytopenia. Based on bone marrow (BM) findings and on liver biopsy showing extramedullary hematopoiesis, an initial diagnosis of PMF was formulated. The patient refused stem cell transplantation from an HLA-identical sibling. Low-dose melphalan was given, without any improvement. Two years later, a BM evaluation showed Gaucher cells. Low glucocerebrosidase and high chitotriosidase levels were indicative for GD. Molecular analysis revealed N370S/complex I mutations. Enzyme replacement therapy with imiglucerase was commenced, resulting in clinical and hematological improvements. Due to an unexpected and persistent organomegaly, PMF combined with GD were suspected. JAK2V617F, JAK2 exon 12, MPL, calreticulin, and exon 9 mutations were negative, and BM examination showed no marrow fibrosis. PMF was excluded. Twenty years after starting treatment, the peripheral cell count and liver size were normal, whereas splenomegaly persisted. Conclusion: In order to avoid a misdiagnosis, a diagnostic algorithm for patients with hepatosplenomegaly combined with cytopenia is suggested

Physicians’ Perceptions of Clinical Utility of a Digital Health Tool for Electronic Patient-Reported Outcome Monitoring in Real-Life Hematology Practice. Evidence From the GIMEMA-ALLIANCE Platform

Digital health tools are increasingly being used in cancer care and may include electronic patient-reported outcome (ePRO) monitoring systems. We examined physicians’ perceptions of usability and clinical utility of a digital health tool (GIMEMA-ALLIANCE platform) for ePRO monitoring in the real-life practice of patients with hematologic malignancies. This tool allows for the collection and assessment of ePROs with real-time graphical presentation of results to medical staff. Based on a predefined algorithm, automated alerts are sent to medical staff. Participating hematologists completed an online survey on their experience with the platform. Of the 201 patients invited to participate between December 2020 and June 2021 (cut-off date for current analysis), 180 (90%) agreed to enter the platform and had a median age of 57 years. Twenty-three hematologists with a median age of 42 years and an average of 17 years of experience in clinical practice were surveyed. All hematologists agreed or strongly agreed that the platform was easy to use, and 87%, agreed or strongly agreed that ePROs data were useful to enhance communication with their patients. The majority of physicians (78%) accessed the platform at least once per month to consult the symptom and health status profile of their patients. The frequency of access was independent of physician sex (p=0.393) and years of experience in clinical practice (p=0.404). In conclusion, our preliminary results support the clinical utility, from the perspective of the treating hematologist, of integrating ePROs into the routine cancer care of patients with hematologic malignancies

Usefulness of bronchoalveolar lavage in suspect COVID-19 repeatedly negative swab test and interstitial lung disease

The diagnosis of coronavirus disease 2019 (COVID-19) relies on nasopharyngeal swab, which shows a 20–30% risk of false negativity [1]. Bronchoalveolar lavage (BAL) is reported to be useful in patients with pulmonary interstitial infiltrates on high-resolution computed tomography (HRCT). We investigated the usefulness of BAL in symptomatic patients with positive HRCT and a repeatedly negative swab test (‘grey zone’)

A Tool for Daily Demand Pattern Generation

A correct water demand characterization is at the base of a reliable water distribution system simulation. The stochastic nature of water demand is well established and thus has to be addressed. In the present work a methodology to generate synthetic demand patterns interpolating known points by means of piecewise interpolation has been implemented in Python. Subsequently a stochastic approach has been applied to the interpolated demand patterns, which is based on a mixed probability distribution. Such approach considers the dual nature of water demand as continuous and discrete random variable, in order to contemplate both the event of it being null and not null. The needed parameters are obtainable through simple equations depending solely on the number of served user