Primary care management of Long-Term opioid therapy.

The United States underwent massive expansion in opioid prescribing from 1990-2010, followed by opioid stewardship initiatives and reduced prescribing. Opioids are no longer considered first-line therapy for most chronic pain conditions and clinicians should first seek alternatives in most circumstances. Patients who have been treated with opioids long-term should be managed differently, sometimes even continued on opioids due to physiologic changes wrought by long-term opioid therapy and documented risks of discontinuation. When providing long-term opioid therapy, clinicians should document opioid stewardship measures, including assessments, consents, medication reconciliation, and offering naloxone, along with the rationale to continue opioid therapy. Clinicians should screen regularly for opioid use disorder and arrange for or directly provide treatment. In particular, buprenorphine can be highly useful for co-morbid pain and opioid use disorder. Addressing other substance use disorders, as well as preventive health related to substance use, should be a priority in patients with opioid use disorder. Patient-centered practices, such as shared decision-making and attending to related facets of a patient's life that influence health outcomes, should be implemented at all points of care.Key messagesAlthough opioids are no longer considered first-line therapy for most chronic pain, management of patients already taking long-term opioid therapy must be individualised.Documentation of opioid stewardship measures can help to organise opioid prescribing and protect clinicians from regulatory scrutiny.Management of resultant opioid use disorder should include provision of medications, most often buprenorphine, and several additional screening and preventive measures.

Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring.

Generic simulation code for spiking neuronal networks spends the major part of the time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval between communication steps by source neurons distributed across many compute nodes and are inherently irregular and unsorted with respect to their targets. For finding those targets, the spikes need to be dispatched to a three-dimensional data structure with decisions on target thread and synapse type to be made on the way. With growing network size, a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source. Here, we show analytically how this sparsity emerges over the practically relevant range of network sizes from a hundred thousand to a billion neurons. By profiling a production code we investigate opportunities for algorithmic changes to avoid indirections and branching. Every thread hosts an equal share of the neurons on a compute node. In the original algorithm, all threads search through all spikes to pick out the relevant ones. With increasing network size, the fraction of hits remains invariant but the absolute number of rejections grows. Our new alternative algorithm equally divides the spikes among the threads and immediately sorts them in parallel according to target thread and synapse type. After this, every thread completes delivery solely of the section of spikes for its own neurons. Independent of the number of threads, all spikes are looked at only two times. The new algorithm halves the number of instructions in spike delivery which leads to a reduction of simulation time of up to 40 %. Thus, spike delivery is a fully parallelizable process with a single synchronization point and thereby well suited for many-core systems. Our analysis indicates that further progress requires a reduction of the latency that the instructions experience in accessing memory. The study provides the foundation for the exploration of methods of latency hiding like software pipelining and software-induced prefetching.

Modeling Patient-Specific Magnetic Drug Targeting Within the Intracranial Vasculature.

Drug targeting promises to substantially enhance future therapies, for example through the focussing of chemotherapeutic drugs at the site of a tumor, thus reducing the exposure of healthy tissue to unwanted damage. Promising work on the steering of medication in the human body employs magnetic fields acting on nanoparticles made of paramagnetic materials. We develop a computational tool to aid in the optimization of the physical parameters of these particles and the magnetic configuration, estimating the fraction of particles reaching a given target site in a large patient-specific vascular system for different physiological states (heart rate, cardiac output, etc.). We demonstrate the excellent computational performance of our model by its application to the simulation of paramagnetic-nanoparticle-laden flows in a circle of Willis geometry obtained from an MRI scan. The results suggest a strong dependence of the particle density at the target site on the strength of the magnetic forcing and the velocity of the background fluid flow.

Comparative analysis of multiple genome-scale data sets.

The ongoing analyses of published genome-scale data sets is evidence that different approaches are required to completely mine this data. We report the use of novel tools for both visualization and data set comparison to analyze yeast gene-expression (cell cycle and exit from stationary phase/G(0)) and protein-interaction studies. This analysis led to new insights about each data set. For example, G(1)-regulated genes are not co-regulated during exit from stationary phase, indicating that the cells are not synchronized. The tight clustering of other genes during exit from stationary-phase data set further indicates the physiological responses during G(0) exit are separable from cell-cycle events. Comparison of the two data sets showed that ribosomal-protein genes cluster tightly during exit from stationary phase, but are found in three significantly different clusters in the cell-cycle data set. Two protein-interaction data sets were also compared with the gene-expression data. Visual analysis of the complete data sets showed no clear correlation between co-expression of genes and protein interactions, in contrast to published reports examining subsets of the protein-interaction data. Neither two-hybrid study identified a large number of interactions between ribosomal proteins, consistent with recent structural data, indicating that for both data sets, the identification of false-positive interactions may be lower than previously thought.