Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

Two Earth-sized planets orbiting Kepler-20

Since the discovery of the first extrasolar giant planets around Sun-like stars, evolving observational capabilities have brought us closer to the detection of true Earth analogues. The size of an exoplanet can be determined when it periodically passes in front of (transits) its parent star, causing a decrease in starlight proportional to its radius. The smallest exoplanet hitherto discovered has a radius 1.42 times that of the Earth's radius (R Earth), and hence has 2.9 times its volume. Here we report the discovery of two planets, one Earth-sized (1.03R Earth) and the other smaller than the Earth (0.87R Earth), orbiting the star Kepler-20, which is already known to host three other, larger, transiting planets. The gravitational pull of the new planets on the parent star is too small to measure with current instrumentation. We apply a statistical method to show that the likelihood of the planetary interpretation of the transit signals is more than three orders of magnitude larger than that of the alternative hypothesis that the signals result from an eclipsing binary star. Theoretical considerations imply that these planets are rocky, with a composition of iron and silicate. The outer planet could have developed a thick water vapour atmosphere.Comment: Letter to Nature; Received 8 November; accepted 13 December 2011; Published online 20 December 201

3D Protein structure prediction with genetic tabu search algorithm

Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively

A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

Stable Isotope Composition of Fatty Acids in Organisms of Different Trophic Levels in the Yenisei River

We studied four-link food chain, periphytic microalgae and water moss (producers), trichopteran larvae (consumers I), gammarids (omnivorous – consumers II) and Siberian grayling (consumers III) at a littoral site of the Yenisei River on the basis of three years monthly sampling. Analysis of bulk carbon stable isotopes and compound specific isotope analysis of fatty acids (FA) were done. As found, there was a gradual depletion in 13C contents of fatty acids, including essential FA upward the food chain. In all the trophic levels a parabolic dependence of δ13C values of fatty acids on their degree of unsaturation/chain length occurred, with 18:2n-6 and 18:3n-3 in its lowest point. The pattern in the δ13C differences between individual fatty acids was quite similar to that reported in literature for marine pelagic food webs. Hypotheses on isotope fractionation were suggested to explain the findings

Detection of ALK fusion transcripts in FFPE lung cancer samples by NanoString technology

Background: ALK-rearranged lung cancers exhibit specific pathologic and clinical features and are responsive to anti-ALK therapies. Therefore, the detection of ALK-rearrangement is fundamental for personalized lung cancer therapy. Recently, new molecular techniques, such as NanoString nCounter, have been developed to detect ALK fusions with more accuracy and sensitivity. Methods: In the present study, we intended to validate a NanoString nCounter ALK-fusion panel in routine biopsies of FFPE lung cancer patients. A total of 43 samples were analyzed, 13 ALK-positive and 30 ALK-negative, as previously detected by FISH and/or immunohistochemistry. Results: The NanoString panel detected the presence of the EML4-ALK, KIF5B-ALK and TFG-ALK fusion variants. We observed that all the 13 ALK-positive cases exhibited genetic aberrations by the NanoString methodology. Namely, six cases (46.15%) presented EML-ALK variant 1, two (15.38%) presented EML-ALK variant 2, two (15.38%) presented EML-ALK variant 3a, and three (23.07%) exhibited no variant but presented unbalanced expression between 5'/3' exons, similar to other positive samples. Importantly, for all these analyses, the initial input of RNA was 100 ng, and some cases displayed poor RNA quality measurements. Conclusions: In this study, we reported the great utility of NanoString technology in the assessment of ALK fusions in routine lung biopsies of FFPE specimens.This study was partially funded by FINEP (MCTI/FINEP/MS/SCTIE/DECIT), Brazil. BIOPLAT (1302/13).info:eu-repo/semantics/publishedVersio

Uncovering Blind Spots in Urban Carbon Management: The Role of Consumption-Based Carbon Accounting in Bristol, UK

The rapid urbanisation of the twentieth century, along with the spread of high-consumption urban lifestyles, has led to cities becoming the dominant drivers of global anthropogenic greenhouse gas emissions. Reducing these impacts is crucial, but production-based frameworks of carbon measurement and mitigation—which encompass only a limited part of cities’ carbon footprints—are much more developed and widely applied than consumption-based approaches that consider the embedded carbon effectively imported into a city. Frequently, therefore, cities are left blind to the importance of their wider consumption-related climate impacts, while at the same time left lacking effective tools to reduce them. To explore the relevance of these issues, we implement methodologies for assessing production- and consumption-based emissions at the city-level and estimate the associated emissions trajectories for Bristol, a major UK city, from 2000 to 2035. We develop mitigation scenarios targeted at reducing the former, considering potential energy, carbon and financial savings in each case. We then compare these mitigation potentials with local government ambitions and Bristol’s consumption-based emissions trajectory. Our results suggest that the city’s consumption-based emissions are three times the production-based emissions, largely due to the impacts of imported food and drink. We find that low-carbon investments of circa £3 billion could reduce production-based emissions by 25% in 2035. However, we also find that this represents <10% of Bristol’s forecast consumption-based emissions for 2035 and is approximately equal to the mitigation achievable by eliminating the city’s current levels of food waste. Such observations suggest that incorporating consumption-based emission statistics into cities’ accounting and decision-making processes could uncover largely unrecognised opportunities for mitigation that are likely to be essential for achieving deep decarbonisation

Effects of patient selection on the applicability of results from a randomised clinical trial (EORTC 10853) investigating breast-conserving therapy for DCIS

Selection of patients for randomised clinical trials may have a large impact on the applicability of the study results to the general population presenting the same disorder. However, clinical characteristics and outcome data on non-entered patients are usually not available. The effects of patient selection for the EORTC 10853 trial investigating the role of radiotherapy in breast conserving therapy for ductal carcinoma in situ have been studied, in an analysis of all patients treated for ductal carcinoma in situ in five participating institutes. The reasons for not entering patients were evaluated and treatment results of the randomised patients were compared to those not entered. A total of 910 patients were treated for ductal carcinoma in situ. Of these, 477 (52%) were ineligible, with the size of the lesion being the main reason for ineligibility (30% of all ductal carcinoma in situ). Of the 433 eligible patients, 278 (64%) were randomised into the trial. The main reasons for non-entry of eligible patients were either physicians' preference for one of the treatment arms (26%) or patients' refusal (9%). These percentages showed significant variation among the institutes. At 4 years follow-up, those patients not entered in the trial and treated with local excision and radiotherapy, had higher local recurrence rates than the patients randomised in the trial and treated with the same approach, (17 vs 2%, P=0.03). The patients treated with local excision alone had equal local recurrence rates (11% in both groups). Selection of patients may explain the differences in outcome of the randomised patients, and those not-entered. Thus, the results of this trial may not be applicable to all patients with ductal carcinoma in situ

Anatomical Global Spatial Normalization

Anatomical global spatial normalization (aGSN) is presented as a method to scale high-resolution brain images to control for variability in brain size without altering the mean size of other brain structures. Two types of mean preserving scaling methods were investigated, “shape preserving” and “shape standardizing”. aGSN was tested by examining 56 brain structures from an adult brain atlas of 40 individuals (LPBA40) before and after normalization, with detailed analyses of cerebral hemispheres, all gyri collectively, cerebellum, brainstem, and left and right caudate, putamen, and hippocampus. Mean sizes of brain structures as measured by volume, distance, and area were preserved and variance reduced for both types of scale factors. An interesting finding was that scale factors derived from each of the ten brain structures were also mean preserving. However, variance was best reduced using whole brain hemispheres as the reference structure, and this reduction was related to its high average correlation with other brain structures. The fractional reduction in variance of structure volumes was directly related to ρ2, the square of the reference-to-structure correlation coefficient. The average reduction in variance in volumes by aGSN with whole brain hemispheres as the reference structure was approximately 32%. An analytical method was provided to directly convert between conventional and aGSN scale factors to support adaptation of aGSN to popular spatial normalization software packages