Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine.

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging.

Avoidable drug-gene conflicts and polypharmacy interactions in patients participating in a personalized medicine program.

AIM: Determine the ability of a pharmacogenetic service, PRIMER, to identify drug-gene (DGI) and drug-drug interactions (DDI) in patients across multiple conditions. PRIMER consists of patient selection criteria, a gene panel and actionable guidance for DGIs and DDIs. RESULTS: The average patient was prescribed 12 medications. PRIMER identified significant DGIs in 73% of patients tested, with 43% having more than one DGI. DDIs were found in 87% of patients. The most common actionable DGIs were for opioid, psychotropic and cardiovascular medications. CONCLUSION: The pairing of patient selection criteria, a multigene panel with evidence-based interpretation and review of DDIs maximizes the patients tested who have actionable benefit and alerts physicians to potentially critical adjustments needed for the patient's medication regimen.

The Pharmacist's Perspective on Pharmacogenetics Implementation.

The future for pharmacogenetics will continue to expand. Pharmacists can apply and incorporate drug knowledge in collaboration with other health providers using pharmacogenetics. Patients benefit with enhanced therapeutic outcomes that could lead to more streamlined drug approaches, fewer follow-up visits, cost savings, and shorter times to achieve therapeutic outcomes. As more drug-gene pathways are discovered and use of this knowledge increases, the potential for algorithm development for medication use will occur, resulting in better patient outcomes, higher standard of care, and reflect evidence-based medicine.

Care of the patient with pulmonary arterial hypertension.

Patients presenting with pulmonary arterial hypertension (PAH), the rarest of the groups of pulmonary hypertension diagnoses, are infrequently seen in the critical care arena. However, when patients with PAH present in the intensive care unit, it is generally related to an exhaustion of treatments. This article focuses on the current state of the literature addressing the group designation, pathophysiology, symptom expression, and treatment modalities of the patient with PAH.