Fusion evaporation-residue cross sections for Si28+40Ca at E(28Si)=309, 397, and 452 MeV

Velocity distributions of mass-identified evaporation residues produced in the Si28+40Ca reaction have been measured at bombarding energies of 309, 397, and 452 MeV using time-of-flight techniques. These distributions were used to identify evaporation residues and to separate the complete-fusion and incomplete-fusion components. Angular distributions and upper limits for the total evaporation-residue and complete-fusion evaporation-residue cross sections were extracted at all three bombarding energies. The complete-fusion evaporation-residue cross sections and the deduced critical angular momenta are compared with earlier measurements and the predictions of existing models. The ratios of the complete-fusion evaporation-residue cross section to the total evaporation-residue cross section, along with those measured for the Si28+12C and Si28+28Si systems at the same energies, support the entrance-channel mass-asymmetry dependence of the incomplete-fusion evaporation-residue process reported earlier

Energy dependence of fusion evaporation-residue cross sections in the Si28+28Si reaction

Velocity distributions of mass-identified evaporation residues produced in the t28/rSi+28Si reaction have been measured at bombarding energies of 174, 215, 240, 309, 397, and 452 MeV using time-of-flight techniques. These distributions were used to identify evaporation residues and to separate the complete-fusion and incomplete-fusion components. Angular distributions and total cross sections were extracted at all six bombarding energies. The complete-fusion evaporation-residue cross sections and the deduced critical angular momenta are compared with lower energy data and the predictions of existing models

A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches

Review article : the Prothrombin Time Test as a Measure of Bleeding Risk and Prognosis in Liver Disease

Background: Prothrombin time (PT)-derived international normalized ratio (INR) is used to assess bleeding risk and prognosis in cirrhosis, and to guide management of associated coagulation disturbances. Recent studies cast doubt on the validity of the assumptions that form the basis for these applications. Aims: To review and critique the use of the PT-INR in cirrhosis. Methods: Search of the literature. Results: In cirrhosis, there is a decrease in both pro- and anti-coagulants. The PT-INR measures only the activity of procoagulants and fails to capture changes in anticoagulants. It is therefore not surprising that the PT does not predict the bleeding risk. The PT-INR provides a robust measure of liver function but recent data showed INR inter-laboratory variability in this setting. This is not surprising as the INR was validated to normalize results for patients on vitamin-K antagonists, not for cirrhosis. This limitation was not appreciated, but the INR is used to construct the model for end-stage liver disease score to prioritize patients for liver transplantation. Reports showed that model for end-stage liver disease is modified by the thromboplastin used for testing. Conclusions: Alternate tests to predict bleeding risk should be developed. The potential for misuse of the PT-INR should drive the development of alternate algorithms for organ allocation