The predictive value of trabecular bone score (TBS) on whole lumbar vertebrae mechanics: an ex vivo study.

We investigated the association of trabecular bone score (TBS) with microarchitecture and mechanical behavior of human lumbar vertebrae. We found that TBS reflects vertebral trabecular microarchitecture and is an independent predictor of vertebral mechanics. However, the addition of TBS to areal BMD (aBMD) did not significantly improve prediction of vertebral strength. INTRODUCTION: The trabecular bone score (TBS) is a gray-level measure of texture using a modified experimental variogram which can be extracted from dual-energy X-ray absorptiometry (DXA) images. The current study aimed to confirm whether TBS is associated with trabecular microarchitecture and mechanics of human lumbar vertebrae, and if its combination with BMD improves prediction of fracture risk. METHODS: Lumbar vertebrae (L3) were harvested fresh from 16 donors. The anteroposterior and lateral bone mineral content (BMC) and areal BMD (aBMD) of the vertebral body were measured using DXA; then, the TBS was extracted using TBS iNsight software (Medimaps SA, France). The trabecular bone volume (Tb.BV/tissue volume, TV), trabecular thickness (Tb.Th), degree of anisotropy, and structure model index (SMI) were measured using microcomputed tomography. Quasi-static uniaxial compressive testing was performed on L3 vertebral bodies to assess failure load and stiffness. RESULTS: The TBS was significantly correlated to Tb.BV/TV and SMI (râeuro0/00=âeuro0/000.58 and -0.62; pâeuro0/00=âeuro0/000.02, 0.01), but not related to BMC and BMD. TBS was significantly correlated with stiffness (râeuro0/00=âeuro0/000.64; pâeuro0/00=âeuro0/000.007), independently of bone mass. Using stepwise multiple regression models, we failed to demonstrate that the combination of BMD and TBS was better at explaining mechanical behavior than either variable alone. However, the combination TBS, Tb.Th, and BMC did perform better than each parameter alone, explaining 79 % of the variability in stiffness. CONCLUSIONS: In our study, TBS was associated with microarchitecture parameters and with vertebral mechanical behavior, but TBS did not improve prediction of vertebral biomechanical properties in addition to aBMD.

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

Predictive ability of severe rainfall events over Catalonia for the year 2008

This paper analyses the predictive ability of quantitative precipitation forecasts (QPF) and the so-called "poor-man" rainfall probabilistic forecasts (RPF). With this aim, the full set of warnings issued by the Meteorological Service of Catalonia (SMC) for potentially-dangerous events due to severe precipitation has been analysed for the year 2008. For each of the 37 warnings, the QPFs obtained from the limited-area model MM5 have been verified against hourly precipitation data provided by the rain gauge network covering Catalonia (NE of Spain), managed by SMC. For a group of five selected case studies, a QPF comparison has been undertaken between the MM5 and COSMO-I7 limited-area models. Although MM5's predictive ability has been examined for these five cases by making use of satellite data, this paper only shows in detail the heavy precipitation event on the 9¿10 May 2008. Finally, the "poor-man" rainfall probabilistic forecasts (RPF) issued by SMC at regional scale have also been tested against hourly precipitation observations. Verification results show that for long events (>24 h) MM5 tends to overestimate total precipitation, whereas for short events (¿24 h) the model tends instead to underestimate precipitation. The analysis of the five case studies concludes that most of MM5's QPF errors are mainly triggered by very poor representation of some of its cloud microphysical species, particularly the cloud liquid water and, to a lesser degree, the water vapor. The models' performance comparison demonstrates that MM5 and COSMO-I7 are on the same level of QPF skill, at least for the intense-rainfall events dealt with in the five case studies, whilst the warnings based on RPF issued by SMC have proven fairly correct when tested against hourly observed precipitation for 6-h intervals and at a small region scale. Throughout this study, we have only dealt with (SMC-issued) warning episodes in order to analyse deterministic (MM5 and COSMO-I7) and probabilistic (SMC) rainfall forecasts; therefore we have not taken into account those episodes that might (or might not) have been missed by the official SMC warnings. Therefore, whenever we talk about "misses", it is always in relation to the deterministic LAMs' QPFs.

hMMP9 as predictive factor for response and progression free survival in breast cancer patients treated with bevacizumab and pegylated liposomal doxorubicin (PLD)

Background: The anti-angiogenic drug, bevacizumab (Bv), is currently used in the treatment of different malignancies including breast cancer. Many angiogenesis-associated molecules are found in the circulation of cancer patients. Until now, there are no prognostic or predictive factors identified in breast cancer patients treated with Bv. We present here the first results of the prospective monitoring of 6 angiogenesis-related molecules in the peripheral blood of breast cancer patients treated with a combination of Bv and PLD in the phase II trial, SAKK 24/06. Methods: Patients were treated with PLD (20 mg/m2) and Bv (10 mg/kg) on days 1 and 15 of each 4-week cycle for a maximum of 6 cycles, followed by Bv monotherapy maintenance (10 mg/m2 q2 weeks) until progression or severe toxicity. Plasma and serum samples were collected at baseline, after 2 months of therapy, then every 3 months and at treatment discontinuation. Enzyme-linked immunosorbent assays (Quantikine, R&D Systems and Reliatech) were used to measure the expression levels of human vascular endothelial growth factor (hVEGF), placental growth factor (hPlGF), matrix metalloproteinase 9 (hMMP9) and soluble VEGF receptors hsVEGFR-1, hsVEGFR-2 and hsVEGFR-3. The log-transformed data (to reduce the skewness) for each marker was analyzed using an analysis of variance (ANOVA) model to determine if there was a difference between the mean of the subgroups of interest (where α = 0.05). The untransformed data was also analyzed in the same manner as a "sensitivity" check. Results: 132 blood samples were collected in 41 out of 43 enrolled patients. Baseline levels of the molecules were compared to disease status according to RECIST. There was a statistically significant difference in the mean of the log-transformed levels of hMMP9 between responders [CR+PR] versus the mean in patients with PD (p-value=0.0004, log fold change=0.7536), and between patients with disease control [CR+PR+SD] and those with PD (p-value=<0.0001, log fold change=0.81559), with the log-transformed level of hMMP9 being higher for the responder group. The mean of the log-transformed levels of hsVEGFR-1 was statistically significantly different between patients with disease control [CR+PR+SD] and those with PD (p-value=0.0068, log fold change=-0.6089), where the log-transformed level of hsVEGFR-1 was lower for the responder group. The log-transformed level of hMMP9 at baseline was identified as a significant prognostic factor in terms of progression free survival (PFS): p-value=0.0417, hazard ratio (HR)=0.574 with a corresponding 95% confidence interval (0.336 - 0.979)). No strong correlation was shown either between the log-transformed levels of hsVEGF, hPlGF, hsVEGFR-2 or hsVEGFR-3 and clinical response or the occurrence of severe toxicity, or between the levels of the different molecules. Conclusions: Our results suggest that baseline plasma level of the matrix metalloproteinase, hMMP9, could predict tumor response and PFS in patients treated with a combination of Bv and PLD. These data justify further investigation in breast cancer patients treated with anti-angiogenic therapy.

Elderly age is not a negative predictive factor for virological response to therapy with pegylated interferon-α and ribavirin in chronic hepatitis C virus patients.

BACKGROUND & AIMS: Age is frequently discussed as negative host factor to achieve a sustained virological response (SVR) to antiviral therapy of chronic hepatitis C. However, elderly patients often show advanced fibrosis/cirrhosis as known negative predictive factor. The aim of this study was to assess age as an independent predictive factor during antiviral therapy. METHODS: Overall, 516 hepatitis C patients were treated with pegylated interferon-α and ribavirin, thereof 66 patients ≥60 years. We analysed the impact of host factors (age, gender, fibrosis, haemoglobin, previous hepatitis C treatment) and viral factors (genotype, viral load) on SVR per therapy course by performing a generalized estimating equations (GEE) regression modelling, a matched pair analysis and a classification tree analysis. RESULTS: Overall, SVR per therapy course was 42.9 and 26.1%, respectively, in young and elderly patients with hepatitis C virus (HCV) genotypes 1/4/6. The corresponding figures for HCV genotypes 2/3 were 74.4 and 84%. In the GEE model, age had no significant influence on achieving SVR. In matched pair analysis, SVR was not different in young and elderly patients (54.2 and 55.9% respectively; P = 0.795 in binominal test). In classification tree analysis, age was not a relevant splitting variable. CONCLUSIONS: Age is not a significant predictive factor for achieving SVR, when relevant confounders are taken into account. As life expectancy in Western Europe at age 60 is more than 20 years, it is reasonable to treat chronic hepatitis C in selected elderly patients with relevant fibrosis or cirrhosis but without major concomitant diseases, as SVR improves survival and reduces carcinogenesis.

The ability model of emotional intelligence: Searching for valid measures

Current measures of ability emotional intelligence (EI)--including the well-known Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT)--suffer from several limitations, including low discriminant validity and questionable construct and incremental validity. We show that the MSCEIT is largely predicted by personality dimensions, general intelligence, and demographics having multiple R's with the MSCEIT branches up to .66; for the general EI factor this relation was even stronger (Multiple R = .76). As concerns the factor structure of the MSCEIT, we found support for four first-order factors, which had differential relations with personality, but no support for a higher-order global EI factor. We discuss implications for employing the MSCEIT, including (a) using the single branches scores rather than the total score, (b) always controlling for personality and general intelligence to ensure unbiased parameter estimates in the EI factors, and (c) correcting for measurement error. Failure to account for these methodological aspects may severely compromise predictive validity testing. We also discuss avenues for the improvement of ability-based tests.

MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

Drinking Patterns and Their Predictive Factors in CONTROL: a 12-Month Prospective Study in a Sample of Alcohol-Dependent Patients Initiating Treatment.

Aims: To describe the drinking patterns and their baseline predictive factors during a 12-month period after an initial evaluation for alcohol treatment. Methods CONTROL is a single-center, prospective, observational study evaluating consecutive alcohol-dependent patients. Using a curve clustering methodology based on a polynomial regression mixture model, we identified three clusters of patients with dominant alcohol use patterns described as mostly abstainers, mostly moderate drinkers and mostly heavy drinkers. Multinomial logistic regression analysis was used to identify baseline factors (socio-demographic, alcohol dependence consequences and related factors) predictive of belonging to each drinking cluster. ResultsThe sample included 143 alcohol-dependent adults (63.6% males), mean age 44.6 ± 11.8 years. The clustering method identified 47 (32.9%) mostly abstainers, 56 (39.2%) mostly moderate drinkers and 40 (28.0%) mostly heavy drinkers. Multivariate analyses indicated that mild or severe depression at baseline predicted belonging to the mostly moderate drinkers cluster during follow-up (relative risk ratio (RRR) 2.42, CI [1.02-5.73, P = 0.045] P = 0.045), while living alone (RRR 2.78, CI [1.03-7.50], P = 0.044) and reporting more alcohol-related consequences (RRR 1.03, CI [1.01-1.05], P = 0.004) predicted belonging to the mostly heavy drinkers cluster during follow-up. Conclusion In this sample, the drinking patterns of alcohol-dependent patients were predicted by baseline factors, i.e. depression, living alone or alcohol-related consequences and findings that may inform clinicians about the likely drinking patterns of their alcohol-dependent patient over the year following the initial evaluation for alcohol treatment.

On the relationship between connections and the asymptotic properties of predictive distributions

In a recent paper, Komaki studied the second-order asymptotic properties of predictive distributions, using the Kullback-Leibler divergence as a loss function. He showed that estimative distributions with asymptotically efficient estimators can be improved by predictive distributions that do not belong to the model. The model is assumed to be a multidimensional curved exponential family. In this paper we generalize the result assuming as a loss function any f divergence. A relationship arises between alpha connections and optimal predictive distributions. In particular, using an alpha divergence to measure the goodness of a predictive distribution, the optimal shift of the estimate distribution is related to alpha-covariant derivatives. The expression that we obtain for the asymptotic risk is also useful to study the higher-order asymptotic properties of an estimator, in the mentioned class of loss functions.

Factor structure of early smoking experiences and associations with smoking behavior: valence or sensitivity model?

The Early Smoking Experience (ESE) questionnaire is the most widely used questionnaire to assess initial subjective experiences of cigarette smoking. However, its factor structure is not clearly defined and can be perceived from two main standpoints: valence, or positive and negative experiences, and sensitivity to nicotine. This article explores the ESE's factor structure and determines which standpoint was more relevant. It compares two groups of young Swiss men (German- and French-speaking). We examined baseline data on 3,368 tobacco users from a representative sample in the ongoing Cohort Study on Substance Use Risk Factors (C-SURF). ESE, continued tobacco use, weekly smoking and nicotine dependence were assessed. Exploratory structural equation modeling (ESEM) and structural equation modeling (SEM) were performed. ESEM clearly distinguished positive experiences from negative experiences, but negative experiences were divided in experiences related to dizziness and experiences related to irritations. SEM underlined the reinforcing effects of positive experiences, but also of experiences related to dizziness on nicotine dependence and weekly smoking. The best ESE structure for predictive accuracy of experiences on smoking behavior was a compromise between the valence and sensitivity standpoints, which showed clinical relevance.

Selective in vivo visualization of immune-cell infiltration in a mouse model of autoimmune myocarditis by fluorine-19 cardiac magnetic resonance.

BACKGROUND: The goal of this study was to characterize the performance of fluorine-19 ((19)F) cardiac magnetic resonance (CMR) for the specific detection of inflammatory cells in a mouse model of myocarditis. Intravenously administered perfluorocarbons are taken up by infiltrating inflammatory cells and can be detected by (19)F-CMR. (19)F-labeled cells should, therefore, generate an exclusive signal at the inflamed regions within the myocardium. METHODS AND RESULTS: Experimental autoimmune myocarditis was induced in BALB/c mice. After intravenous injection of 2×200 µL of a perfluorocarbon on day 19 and 20 (n=9) after immunization, in vivo (19)F-CMR was performed at the peak of myocardial inflammation (day 21). In 5 additional animals, perfluorocarbon combined with FITC (fluorescein isothiocyanate) was administered for postmortem immunofluorescence and flow-cytometry analyses. Control experiments were performed in 9 animals. In vivo (19)F-CMR detected myocardial inflammation in all experimental autoimmune myocarditis-positive animals. Its resolution was sufficient to identify even small inflammatory foci, that is, at the surface of the right ventricle. Postmortem immunohistochemistry and flow cytometry confirmed the presence of perfluorocarbon in macrophages, dendritic cells, and granulocytes, but not in lymphocytes. The myocardial volume of elevated (19)F signal (rs=0.96; P<0.001), the (19)F signal-to-noise ratio (rs=0.92; P<0.001), and the (19)F signal integral (rs=0.96; P<0.001) at day 21 correlated with the histological myocarditis severity score. CONCLUSIONS: In vivo (19)F-CMR was successfully used to visualize the inflammation specifically and robustly in experimental autoimmune myocarditis, and thus allowed for an unprecedented insight into the involvement of inflammatory cells in the disease process.

Mobile Robot Local Predictive Control under Perception Constraints

This research extends a previously developed work concerning about the use of local model predictive control in mobile robots. Hence, experimental results are presented as a way to improve the methodology by considering aspects as trajectory accuracy and time performance. In this sense, the cost function and the prediction horizon are important aspects to be considered. The platformused is a differential driven robot with a free rotating wheel. The aim of the present work is to test the control method by measuring trajectory tracking accuracy and time performance. Moreover, strategies for the integration with perception system and path planning are also introduced. In this sense, monocular image data provide an occupancy grid where safety trajectories are computed by using goal attraction potential fields

Success of referral for alcohol dependent patients from a general hospital : predictive value of patient and process characteristics

RESUME: Introduction L'objectif de cette étude prospective de cohorte était d'estimer l'efficacité d'un processus de prise en charge standardisé de patients dépendants de l'alcool dans le contexte d'un hôpital universitaire de soins généraux. Ce modèle de prise en charge comprenait une évaluation multidisciplinaire puis des propositions de traitements individualisées et spécifiques (« projet thérapeutique »). Patients et méthode 165 patients alcoolo-dépendants furent recrutés dans différents services de l'hôpital universitaire, y compris la policlinique de médecine. Ils furent dans un premier temps évalués par une équipe multidisciplinaire (médecin interniste, psychiatre, assistant social), puis un projet thérapeutique spécialisé et individualisé leur fut proposé lors d'une rencontre réunissant le patient et l'équipe. Tous les patients éligibles acceptant de participer à l'étude (n=68) furent interrogés au moment de l'inclusion puis 2 et 6 mois plus tard par une psychologue. Des informations standardisées furent recueillies sur les caractéristiques des patients, le processus de prise en charge et l'évolution à 6 mois. Les critères de succès utilisés à 6 mois furent: l'adhérence au traitement proposé et l'abstinence d'alcool. Résultats Lors de l'évaluation à 6 mois, 43% des patients étaient toujours en traitement et 28% étaient abstinents. Les variables prédictrices de succès parmi les caractéristiques des patients étaient un âge de plus de 45 ans, ne pas vivre seul, avoir un travail et être motivé pour un traitement (RAATE-A <18). Pour les variables dépendantes du processus de prise en charge, un sevrage complet de l'alcool lors de la rencontre multidisciplinaire ainsi que la présence de tous les membres de l'équipe à cette réunion étaient des facteurs associés au succès. Conclusion L'efficacité de ce modèle d'intervention pour patients dépendants de l'alcool en hôpital de soins généraux s'est montrée satisfaisante, en particulier pour le critère de succès adhérence au traitement. Des variables associées au succès ou à l'échec à 6 mois ont pu être mises en évidence, permettant d'identifier des populations de patients évoluant différemment. Des stratégies de prise en charge tenant compte de ces éléments pourraient donc être développées, permettant de proposer des traitements plus adaptés ainsi qu'une meilleure rétention des patients alcooliques dans les programmes thérapeutiques. ABSTRACT. To assess the effectiveness of a multidisciplinary evaluation and referral process in a prospective cohort of general hospital patients with alcohol dependence, alcohol-dependent patients were identified in the wards of the general hospital and its primary care center. They were evaluated and then referred to treatment by a multidisciplinary team; those patients who accepted to participate in this cohort study were consecutively included and followed for 6 months. Not included patients were lost for follow-up, whereas all included patients were assessed at time of inclusion, 2 and 6 months later by a research psychologist in order to collect standardized baseline patients' characteristics, process salient features and patients outcomes (defined as treatment adherence and abstinence). Multidisciplinary evaluation and therapeutic referral was feasible and effective, with a success rate of 43% for treatment adherence and 28% for abstinence at 6 months. Among patients' characteristics, predictors of success were an age over 45, not living alone, being employed and being motivated to treatment (RAATE-A score < 18), whereas successful process characteristics included detoxification of the patient at time of referral and a full multidisciplinary referral meeting. This multidisciplinary model of evaluation and referral of alcohol dependent patients of a general hospital had a satisfactory level of effectiveness. Predictors of success and failure allow the identification of subsets of patients for whom new strategies of motivation and treatment referral should be designed.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.