Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.

Incorporating Severity and Risk as Factors to the Fardal Cost-Effectiveness Model to Create a Cost-Benefit Model for Periodontal Treatment

Background: A previously described economic model was based on average values for patients diagnosed with chronic periodontitis (CP). However, tooth loss varies among treated patients and factors for tooth loss include CP severity and risk. The model was refined to incorporate CP severity and risk to determine the cost of treating a specific level of CP severity and risk that is associated with the benefit of tooth preservation.

Methods: A population that received and another that did not receive periodontal treatment were used to determine treatment costs and tooth loss. The number of teeth preserved was the difference of the number of teeth lost between the two populations. The cost of periodontal treatment was divided by the number of teeth preserved for combinations of CP severity and risk.

Results: The cost of periodontal treatment divided by the number of teeth preserved ranged from (US) $ 1,405 to $ 4,895 for high or moderate risk combined with any severity of CP and was more than $ 8,639 for low risk combined with mild CP. The cost of a three-unit bridge was $ 3,416, and the cost of a single-tooth replacement was $ 4,787.

Conclusion: Periodontal treatment could be justified on the sole basis of tooth preservation when CP risk is moderate or high regardless of disease severity.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Risk indicators associated with root decay in independently living older adults

Objective: To determine the risk indicators associated with root caries experience in a cohort of independently living older adults in Ireland.
Methods: The data reported in the present study were obtained from a prospective longitudinal study conducted on the risk factors associated with root caries incidence in a cohort of independently living older adults (n=334). Each subject underwent an oral examination, performed by a single calibrated examiner, to determine the root caries index and other clinical variables. Questionnaires were used to collect data on oral hygiene habits, diet, smoking and alcohol habits and education level. A regression analysis with the outcome variable of root caries experience (no/yes) was conducted.
Results: A total of 334 older adults with a mean age of 69.1 years were examined. 53.3% had at least one filled or decayed root surface. The median root caries index was 3.13 (IQR 0.00, 13.92). The results from the multivariate regression analysis indicated that individuals with poor plaque control (OR 9.59, 95%CI 3.84-24.00), xerostomia (OR 18.49, 95%CI 2.00-172.80), two or more teeth with coronal decay (OR 4.50, 95% CI 2.02-10.02) and 37 or more exposed root surfaces (OR 5.48, 95% CI 2.49-12.01) were more likely to have been affected by root caries.
Conclusions: The prevalence of root caries was high in this cohort. This study suggests a correlation between root caries and the variables poor plaque control, xerostomia, coronal decay (≥2 teeth affected) and exposed root surfaces (≥37). The significance of these risk indicators and the resulting prediction model should be further evaluated in a prospective study of root caries incidence.
Clinical Significance: Identification of risk indicators for root caries in independently living older adults would facilitate dental practitioners to identify those who would benefit most from interventions aimed at prevention.

Area factors and suicide: 5-year follow-up of the Northern Ireland population

Background: Suicide rates vary markedly between areas but it is unclear whether this is due to differences in population composition or to contextual factors operating at an area level.
Aims: To determine if area factors are independently related to suicide risk after adjustment for individual and family characteristics.
Method: A 5-year record linkage study was conducted of 1 116 748 non-institutionalised individuals aged 16-74 years, enumerated at the 2001 Northern Ireland census.
Results: The cohort experienced 566 suicides during follow-up. Suicide risks were lowest for women and for those who were married or cohabiting. Indicators of individual and household disadvantage and economic and health status at the time of the census were also strongly related to risk of suicide. The higher rates of suicide in the more deprived and socially fragmented areas disappeared after adjustment for individual and household factors. There was no significant relationship between population density and risk of suicide.
Conclusions: Differences in rates of suicide between areas are predominantly due to population characteristics rather than to area-level factors, which suggests that policies targeted at area-level factors are unlikely to significantly influence suicides rates.

Contribution of 30 Biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: The MONICA, risk, genetics, archiving and monograph (MORGAM) biomarker project

Background— Cardiovascular risk estimation by novel biomarkers needs assessment in disease-free population cohorts, followed up for incident cardiovascular events, assaying the serum and plasma archived at baseline. We report results from 2 cohorts in such a continuing study.
Methods and Results— Thirty novel biomarkers from different pathophysiological pathways were evaluated in 7915 men and women of the FINRISK97 population cohort with 538 incident cardiovascular events at 10 years (fatal or nonfatal coronary or stroke events), from which a biomarker score was developed and then validated in the 2551 men of the Belfast Prospective Epidemiological Study of Myocardial Infarction (PRIME) cohort (260 events). No single biomarker consistently improved risk estimation in FINRISK97 men and FINRISK97 women and the Belfast PRIME Men cohort after allowing for confounding factors; however, the strongest associations (with hazard ratio per SD in FINRISK97 men) were found for N-terminal pro-brain natriuretic peptide (1.23), C-reactive protein (1.23), B-type natriuretic peptide (1.19), and sensitive troponin I (1.18). A biomarker score was developed from the FINRISK97 cohort with the use of regression coefficients and lasso methods, with selection of troponin I, C-reactive protein, and N-terminal pro-brain natriuretic peptide. Adding this score to a conventional risk factor model in the Belfast PRIME Men cohort validated it by improved c-statistics (P=0.004) and integrated discrimination (P<0.0001) and led to significant reclassification of individuals into risk categories (P=0.0008).
Conclusions— The addition of a biomarker score including N-terminal pro-brain natriuretic peptide, C-reactive protein, and sensitive troponin I to a conventional risk model improved 10-year risk estimation for cardiovascular events in 2 middle-aged European populations. Further validation is needed in other populations and age groups.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

Metabolomic Profiling of In Vivo Plasma Responses to DioxinAssociated Dietary Contaminant Exposure in Rats: Implications for Identification of Sources of Animal and Human Exposure

Dioxin contamination of the food chain typically occurs when cocktails of combustion residues or polychlorinated biphenyl (PCB) containing oils become incorporated into animal feed. These highly toxic compounds are bioaccumulative with small amounts posing a major health risk. The ability to identify animal exposure to these compounds prior to their entry into the food chain may be an invaluable tool to safeguard public health. Dioxin-like compounds act by a common mode of action and this suggests that markers or patterns of response may facilitate identification of exposed animals. However, secondary co-contaminating compounds present in typical dioxin sources may affect responses to compounds. This study has investigated for the first time the potential of a metabolomics platform to distinguish between animals exposed to different sources of dioxin contamination through their diet. Sprague-Dawley rats were given feed containing dioxin-like toxins from hospital incinerator soot, a common PCB oil standard and pure 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (normalized at 0.1 µg/kg TEQ) and acquired plasma was subsequently biochemically profiled using ultra high performance liquid chromatography (UPLC) quadropole time-of-flight-mass spectrometry (QTof-MS). An OPLS-DA model was generated from acquired metabolite fingerprints and validated which allowed classification of plasma from individual animals into the four dietary exposure study groups with a level of accuracy of 97-100%. A set of 24 ions of importance to the prediction model, and which had levels significantly altered between feeding groups, were positively identified as deriving from eight identifiable metabolites including lysophosphatidylcholine (16:0) and tyrosine. This study demonstrates the enormous potential of metabolomic-based profiling to provide a powerful and reliable tool for the detection of dioxin exposure in food-producing animals.

High population prevalence of cardiac troponin I measured by a high-sensitivity assay and cardiovascular risk estimation: the MORGAM Biomarker Project Scottish Cohort

Aims
Our aim was to test the prediction and clinical applicability of high-sensitivity assayed troponin I for incident cardiovascular events in a general middle-aged European population.

Methods and results
High-sensitivity assayed troponin I was measured in the Scottish Heart Health Extended Cohort (n = 15 340) with 2171 cardiovascular events (including acute coronary heart disease and probable ischaemic strokes), 714 coronary deaths (25% of all deaths), 1980 myocardial infarctions, and 797 strokes of all kinds during an average of 20 years follow-up. Detection rate above the limit of detection (LoD) was 74.8% in the overall population and 82.6% in men and 67.0% in women. Troponin I assayed by the high-sensitivity method was associated with future cardiovascular risk after full adjustment such as that individuals in the fourth category had 2.5 times the risk compared with those without detectable troponin I (P < 0.0001). These associations remained significant even for those individuals in whom levels of contemporary-sensitivity troponin I measures were not detectable. Addition of troponin I levels to clinical variables led to significant increases in risk prediction with significant improvement of the c-statistic (P < 0.0001) and net reclassification (P < 0.0001). A threshold of 4.7 pg/mL in women and 7.0 pg/mL in men is suggested to detect individuals at high risk for future cardiovascular events.

Conclusion
Troponin I, measured with a high-sensitivity assay, is an independent predictor of cardiovascular events and might support selection of at risk individuals.

Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.

Sphingosine Kinase 1 Promotes Malignant Progression in Colon Cancer and Independently Predicts Survival of Patients With Colon Cancer by Competing Risk Approach in South Asian Population

OBJECTIVES: Sphingosine kinase 1 (SphK1) phosphorylates the membrane sphingolipid, sphingosine, to sphingosine-1-phosphate (S1P), an oncogenic mediator, which drives tumor cell growth and survival. Although SphK1 has gained increasing prominence as an oncogenic determinant in several cancers, its potential as a therapeutic target in colon cancer remains uncertain. We investigated the clinical relevance of SphK1 expression in colon cancer as well as its inhibitory effects in vitro.

METHODS: SphK1 expression in human colon tumor tissues was determined by immunohistochemistry and its clinicopathological significance was ascertained in 303 colon cancer cases. The effects of SphK1 inhibition on colon cancer cell viability and the phosphoinositide 3-kinase (PI3K)/Akt cell survival pathway were investigated using a SphK1-selective inhibitor-compound 5c (5c). The cytotoxicity of a novel combination using SphK1 inhibition with the chemotherapeutic drug, 5-fluorouracil (5-FU), was also determined.

RESULTS: High SphK1 expression correlated with advanced tumor stages (AJCC classification). Using a competing risk analysis model to take into account disease recurrence, we found that SphK1 is a significant independent predictor for mortality in colon cancer patients. In vitro, the inhibition of SphK1 induced cell death in colon cancer cell lines and attenuated the serum-dependent PI3K/Akt signaling. Inhibition of SphK1 also enhanced the sensitivity of colon cancer cells to 5-FU.

CONCLUSION: Our findings highlight the impact of SphK1 in colon cancer progression and patient survival, and provide evidence supportive of further development in combination strategies that incorporate SphK1 inhibition with current chemotherapeutic agents to improve colon cancer outcomes.

Geotechnical failure risk reviewed

Slope instabilities – commonly triggered by rainfall – pose a geotechnical risk causing disruption to transport routes and incur significant financial resources. This article details laboratory, ground and remote sensing investigations carried out by Queen’s University Belfast and Transport Northern Ireland (TNI) to characterise and monitor slope instability on two higher risk infrastructure slopes in Northern Ireland. The research is used to update a noninvasive risk assessment model of slopes across the country’s road network to direct resources for future investigation.

The Valuation Implications of Enterprise Risk Management Maturity

Enterprise Risk Management (ERM) is the discipline by which enterprises monitor, analyze, and control risks from across the enterprise, with the goal of identifying underlying correlations and thus optimizing the risk-taking behavior in a portfolio context. This study analyzes the valuation implications of ERM Maturity. We use data from the industry leading Risk and Insurance Management Society Risk Maturity Model over the period from 2006 to 2011, which scores firms on a five-point maturity scale. Our results suggest that firms that have reached mature levels of ERM are exhibiting a higher firm value, as measured by Tobin's Q. We find a statistically significant positive relation to the magnitude of 25 percent. Upon decomposition of the maturity score, we find that the most important aspects of ERM from a valuation perspective relate to the level of top–down executive engagement and the resultant cascade of ERM culture throughout the firm. Firms that have successfully integrated the ERM process into both their strategic activities and everyday practices display superior ability in uncovering risk dependencies and correlations across the entire enterprise and as a consequence enhanced value when undertaking the ERM maturity journey ceteris paribus.

Monitoring ocular hypertension, how much and how often? A cost-effectiveness perspective

OBJECTIVE: To assess the efficiency of alternative monitoring services for people with ocular hypertension (OHT), a glaucoma risk factor.

DESIGN: Discrete event simulation model comparing five alternative care pathways: treatment at OHT diagnosis with minimal monitoring; biennial monitoring (primary and secondary care) with treatment if baseline predicted 5-year glaucoma risk is ≥6%; monitoring and treatment aligned to National Institute for Health and Care Excellence (NICE) glaucoma guidance (conservative and intensive).

SETTING: UK health services perspective.

PARTICIPANTS: Simulated cohort of 10 000 adults with OHT (mean intraocular pressure (IOP) 24.9 mm Hg (SD 2.4).

MAIN OUTCOME MEASURES: Costs, glaucoma detected, quality-adjusted life years (QALYs).

RESULTS: Treating at diagnosis was the least costly and least effective in avoiding glaucoma and progression. Intensive monitoring following NICE guidance was the most costly and effective. However, considering a wider cost-utility perspective, biennial monitoring was less costly and provided more QALYs than NICE pathways, but was unlikely to be cost-effective compared with treating at diagnosis (£86 717 per additional QALY gained). The findings were robust to risk thresholds for initiating monitoring but were sensitive to treatment threshold, National Health Service costs and treatment adherence.

CONCLUSIONS: For confirmed OHT, glaucoma monitoring more frequently than every 2 years is unlikely to be efficient. Primary treatment and minimal monitoring (assessing treatment responsiveness (IOP)) could be considered; however, further data to refine glaucoma risk prediction models and value patient preferences for treatment are needed. Consideration to innovative and affordable service redesign focused on treatment responsiveness rather than more glaucoma testing is recommended.

High-pressure volumetric properties of imidazolium-based ionic liquids: Effect of the anion

The densities of five imidazolium-based ionic liquids (ILs) (1-butyl-3-methylimidazolium tetrafluoroborate, [CiC4-Im][BF 4]; 1-butyl-3-methylimidazolium hexafluorophosphate, [CiC 4Im][PF6]; 1-butyl-3-methylimidazolium bis{(trifluoromethyl)sulfonyl}imide, [C1C4Im][Tf 2N]; 1-ethyl-3-methylimidazoliumbis{(trifluoromethyl)sulfonyl}-imide, [C1C2Im][Tf2N]; l-ethyl-3-methylimidazolium ethylsulfate, [C1C2Im][EtSO4]) were measured as a function of temperature from (293 to 415) K and over an extended pressure range from (0.1 to 40) MPa using a vibratingtube densimeter. Knowledge of the variation of the density with temperature and pressure allows access to the mechanical coefficients: thermal expansion coefficient and isothermal compressibility. The effects of the anion and of the length of the alkyl chain on the imidazolium ring on the volumetric properties were particularly examined. The mechanical coefficients were compared with those of common organic solvents, water and liquid NaCl. Finally, a prediction model, based on an "ideal" volumetric behavior of the ILs, is proposed to allow calculation of the molar volume of imidazolium-based ionic liquids as a function of temperature. ©2007 American Chemical Society.