Combination of RNase Binase and AKT1/2 Kinase Inhibitor Blocks Two Alternative Survival Pathways in Kasumi-1 Cells

Molecular Biology - Treatment of malignant neoplasms often requires the use of combinations of chemotherapeutic agents. However, in order to select combinations that are effective against specific...     

Peptide coding capacity of polysomal and non-polysomal messenger RNA during growth of animal cells.

The major peptides encoded by cytoplasmic RNA preparation translated in vitro in extracts of wheat germ were displayed by two-dimensional electrophoresis on polyacrylamide gels. With this assay system polysomal and non-polysomal RNA preparations were found to differ in coding capacity. These differences tended to be greater in RNA preparations from stationary phase cells than in those from exponential phase cells. These differences were maintained when the concentrations of potassium and magnesium were above or below the optimal concentrations for incorporation. Most of the messenger RNA activities preferentially in the post-polysomal region could be driven into the polysomal region in the presence of cycloheximide. We conclude that these measurements are valid measurements of concentrations of individual functional mRNA species in these RNA preparations.     

Dynamic prediction: A challenge for biostatisticians,but greatly needed by patients,physicians and the public

Prognosis is usually expressed in terms of the probability that a patient will or will not have experienced an event of interest t years after diagnosis of a disease. This quantity, however, is of little informative value for a patient who is still event-free after a number of years. Such a patient would be much more interested in the conditional probability of being event-free in the upcoming t years, given that he/she did not experience the event in the s years after diagnosis, called “conditional survival.” It is the simplest form of a dynamic prediction and can be dealt with using straightforward extensions of standard time-to-event analyses in clinical cohort studies. For a healthy individual, a related problem with further complications is the so-called “age-conditional probability of developing cancer” in the next t years. Here, the competing risk of dying from other diseases has to be taken into account. For both situations, the hazard function provides the central dynamic concept, which can be further extended in a natural way to build dynamic prediction models that incorporate both baseline and time-dependent characteristics. Such models are able to exploit the most current information accumulating over time in order to accurately predict the further course or development of a disease. In this article, the biostatistical challenges as well as the relevance and importance of dynamic prediction are illustrated using studies of multiple myeloma, a hematologic malignancy with a formerly rather poor prognosis which has improved over the last few years.