Inter-Tumor Heterogeneity-Melanomas Respond Differently to GM-CSF-Mediated Activation.

Granulocyte-monocyte colony stimulating factor (GM-CSF) is used as an adjuvant in various clinical and preclinical studies with contradictory results. These were attributed to opposing effects of GM-CSF on the immune or myeloid systems of the treated patients or to lack of optimal dosing regimens. The results of the present study point to inter-tumor heterogeneity as a possible mechanism accounting for the contrasting responses to GM-CSF incorporating therapies. Employing xenograft models of human melanomas in nude mice developed in our lab, we detected differential functional responses of melanomas from different patients to GM-CSF both in vitro as well as in vivo. Whereas cells of one melanoma acquired pro metastatic features following exposure to GM-CSF, cells from another melanoma either did not respond or became less malignant. We propose that inter-melanoma heterogeneity as manifested by differential responses of melanoma cells (and perhaps also of other tumor) to GM-CSF may be developed into a predictive marker providing a tool to segregate melanoma patients who will benefit from GM-CSF therapy from those who will not

Improving Diagnostic Accuracy Using EHR in Emergency Departments: A Simulation-Based Study

It is widely believed that electronic health records (EHR) improve medical decision making by enabling medical staff to access medical information stored in the system. It remains unclear, however, whether EHR indeed fulfills this claim under the severe time constraints of Emergency Departments (EDs). We assessed whether accessing EHR in an ED actually improves decision-making by clinicians. An authentic simulated ED environment was created at the Israel Center for Medical Simulation (MSR). Four different actors were trained to simulate four specific complaints and behavior. Each physician treated half of the cases (randomly) with access to EHR, and their medical decisions were compared to those where the physicians had no access to EHR. Accessing the EHR led to an increase in the quality of the clinical decisions. The percentage of correct diagnoses was higher and physicians were more confident in their diagnoses

Detecting and Exploiting Near-Sortedness for Efficient Relational Query Evaluation

Many relational operations are best performed when the relations are stored sorted over the relevant attributes (e.g. the common attributes in a natural join operation). However, generally relations are not stored sorted because it is expensive to maintain them this way (and impossible whenever there is more than one relevant sort key). Still, many times relations turn out to be nearly-sorted, where most tuples are close to their place in the order. This state can result from “leftover sortedness”, where originally sorted relations were updated, or were combined into interim results when evaluating a complex query. It can also result from weak correlations between attribute values. Currently, nearly-sorted relations are treated the same as unsorted relations, and when relational operations are evaluated for them, a generic algorithm is used. Yet, many operations can be computed more efficiently by an algorithm that exploits this near-ordering. However, to consistently benefit from using such algorithms the system should also refrain from using the wrong algorithm for relations which happen not to be sorted at all. Thus, an efficient test is required, i.e., a very fast approximation algorithm for establishing whether a given relation is sufficiently nearly-sorted. In this paper, we provide the theoretical foundations for improving query evaluation over possibly nearly-sorted relations. First we formally define what it means for a relation to be nearly-sorted, and show how operations over such relations, such as natural join, set operations and sorting, can be executed significantly more efficiently using an algorithm that we provide. If a relation is nearly-sorted enough, then it can be sorted using two sequential reads of the relation, and writing no intermediate data to disk. We then construct efficient probabilistic tests for approximating the degree of the near-sortedness of a relation without having to read an entire file. The role of our algorithms in a database manage