Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival

Physical Modeling of Snow Gliding: A Case Study in the NW Italian Alps

Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding

Understanding Factors Associated With Psychomotor Subtypes of Delirium in Older Inpatients With Dementia

Objectives: Few studies have analyzed factors associated with delirium subtypes. In this study, we investigate factors associated with subtypes of delirium only in patients with dementia to provide insights on the possible prevention and treatments. Design: This is a cross-sectional study nested in the “Delirium Day” study, a nationwide Italian point-prevalence study. Setting and Participants: Older patients admitted to 205 acute and 92 rehabilitation hospital wards. Measures: Delirium was evaluated with the 4-AT and the motor subtypes with the Delirium Motor Subtype Scale. Dementia was defined by the presence of a documented diagnosis in the medical records and/or prescription of acetylcholinesterase inhibitors or memantine prior to admission. Results: Of the 1057 patients with dementia, 35% had delirium, with 25.6% hyperactive, 33.1% hypoactive, 34.5% mixed, and 6.7% nonmotor subtype. There were higher odds of having venous catheters in the hypoactive (OR 1.82, 95% CI 1.18-2.81) and mixed type of delirium (OR 2.23, CI 1.43-3.46), whereas higher odds of urinary catheters in the hypoactive (OR 2.91, CI 1.92-4.39), hyperactive (OR 1.99, CI 1.23-3.21), and mixed types of delirium (OR 2.05, CI 1.36-3.07). We found higher odds of antipsychotics both in the hyperactive (OR 2.87, CI 1.81-4.54) and mixed subtype (OR 1.84, CI 1.24-2.75), whereas higher odds of antibiotics was present only in the mixed subtype (OR 1.91, CI 1.26-2.87). Conclusions and Implications: In patients with dementia, the mixed delirium subtype is the most prevalent followed by the hypoactive, hyperactive, and nonmotor subtype. Motor subtypes of delirium may be triggered by clinical factors, including the use of venous and urinary catheters, and the use of antipsychotics. Future studies are necessary to provide further insights on the possible pathophysiology of delirium in patients with dementia and to address the optimization of the management of potential risk factors