1,721,265 research outputs found
Comments on the paper: "On the co-evolution of innovation and demand" by P. P. Saviotti and A. Pyka
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What meets the eye: The effect of the presence of immigrants on personal attitudes to migrations in Europe
No full textPrognostic relevance of 18-F FDG PET/CT in newly diagnosed multiple myeloma patients treated with up-front autologous transplantation (Blood (2011) 118, 23 (5989-5995))
No full textWe prospectively analyzed the prognostic relevance of positron emission tomography-computed tomography (PET/CT) at diagnosis, after thalidomide-dexamethasone (TD) induction therapy and double autotransplantation (ASCT) in 192 newly diagnosed multiple myeloma (MM) patients. Presence at baseline of at least 3 focal lesions (FLs; 44% of cases), a standardized uptake value (SUV) > 4.2 (46%), and extramedullary disease (EMD; 6%) adversely affected 4-year estimates of progression-free survival (PFS; ≥ 3 FLs: 50%; SUV > 4.2: 43%; presence of EMD: 28%). SUV > 4.2 and EMD were also correlated with shorter overall survival (OS; 4-year rates: 77% and 66%, respectively). Persistence of SUV > 4.2 after TD induction was an early predictor for shorter PFS. Three months after ASCT, PET/CT was negative in 65% of patients whose 4-year rates of PFS and OS were superior to those of PET-positive patients (PFS: 66% and OS: 89%). In a multivariate analysis, both EMD and SUV > 4.2 at baseline and persistence of fluorodeoxyglucose (FDG) uptake after ASCT were independent variables adversely affecting PFS. PET/CT involvement at diagnosis, after novel agent-based induction and subsequent ASCT is a reliable predictor of prognosis in MM patients. This study is registered at www.clinicaltrials.gov as NTC01341262
Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures
No full textIdentifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus’s infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred
The role of monoclonal antibodies in smoldering and newly diagnosed transplant-eligible multiple myeloma
Full text linkThe recent introduction of monoclonal antibodies (MoAbs), with several cellular targets, such as CD-38 (daratumumab and isatuximab) and SLAM F7 (elotuzumab), differently combined with other classes of agents, has significantly extended the outcomes of patients with multiple myeloma (MM) in different phases of the disease. Initially used in advanced/refractory patients, different MoAbs combination have been introduced in the treatment of newly diagnosed transplant eligible patients (NDTEMM), showing a significant improvement in the depth of the response and in survival outcomes, without a significant price in terms of toxicity. In smoldering MM, MoAbs have been applied, either alone or in combination with other drugs, with the goal of delaying the progression to active MM and restoring the immune system. In this review, we will focus on the main results achieved so far and on the main on-going trials using MoAbs in SMM and NDTEMM
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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