Meaning of the λ parameter of skew–normal and log–skew normal distributions in fluid geochemistry

The literature related to skew–normal distributions has grown rapidly in recent yearsbut at the moment few applications concern the description of natural phenomena withthis type of probability models, as well as the interpretation of their parameters. Theskew–normal distributions family represents an extension of the normal family to whicha parameter (λ) has been added to regulate the skewness. The development of this theoreticalfield has followed the general tendency in Statistics towards more flexible methodsto represent features of the data, as adequately as possible, and to reduce unrealisticassumptions as the normality that underlies most methods of univariate and multivariateanalysis. In this paper an investigation on the shape of the frequency distribution of thelogratio ln(Cl−/Na+) whose components are related to waters composition for 26 wells,has been performed. Samples have been collected around the active center of Vulcanoisland (Aeolian archipelago, southern Italy) from 1977 up to now at time intervals ofabout six months. Data of the logratio have been tentatively modeled by evaluating theperformance of the skew–normal model for each well. Values of the λ parameter havebeen compared by considering temperature and spatial position of the sampling points.Preliminary results indicate that changes in λ values can be related to the nature ofenvironmental processes affecting the data

Uitlité clinique du lavage bronchoalvéolaire [Clinical value of bronchoalveolar lavage].

Bronchoalveolar lavage (BAL) is a minimally invasive procedure used to characterize the status of the alveolar space. Standardization of the procedure and the analysis of samples taken is essential for their proper interpretation. In nonresolving or ventilator-associated pneumonia, BAL contributes to the detection of resistant pathogens and noninfectious etiologies. In immunocompromised hosts with radiological infiltrates, BAL should be performed early during work-up since outcome is significantly modified in this population group. In cases of interstitial lung disease, BAL can exclude infectious or neoplastic causes. Associated with a clinical and radiological evaluation, it provides valuables additional diagnostic information.

Angiogenesis-related gene expression profile with independent prognostic value in advanced ovarian carcinoma.

BACKGROUND Ovarian carcinoma is the most important cause of gynecological cancer-related mortality in Western societies. Despite the improved median overall survival in patients receiving chemotherapy regimens such as paclitaxel and carboplatin combination, relapse still occurs in most advanced diseased patients. Increased angiogenesis is associated with rapid recurrence and decreased survival in ovarian cancer. This study was planned to identify an angiogenesis-related gene expression profile with prognostic value in advanced ovarian carcinoma patients. METHODOLOGY/PRINCIPAL FINDINGS RNAs were collected from formalin-fixed paraffin-embedded samples of 61 patients with III/IV FIGO stage ovarian cancer who underwent surgical cytoreduction and received a carboplatin plus paclitaxel regimen. Expression levels of 82 angiogenesis related genes were measured by quantitative real-time polymerase chain reaction using TaqMan low-density arrays. A 34-gene-profile which was able to predict the overall survival of ovarian carcinoma patients was identified. After a leave-one-out cross validation, the profile distinguished two groups of patients with different outcomes. Median overall survival and progression-free survival for the high risk group was 28.3 and 15.0 months, respectively, and was not reached by patients in the low risk group at the end of follow-up. Moreover, the profile maintained an independent prognostic value in the multivariate analysis. The hazard ratio for death was 2.3 (95% CI, 1.5 to 3.2; p<0.001). CONCLUSIONS/SIGNIFICANCE It is possible to generate a prognostic model for advanced ovarian carcinoma based on angiogenesis-related genes using formalin-fixed paraffin-embedded samples. The present results are consistent with the increasing weight of angiogenesis genes in the prognosis of ovarian carcinoma.

Total hemoglobin mass--a new parameter to detect blood doping?

PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

Prédiction de la réponse aux SSRI chez les patients avec épisode majeur de dépression par l'étude du métabolisme cérébral par PET/CT au F-18-FDG

Contexte : Les patients souffrant d'un épisode dépressif sévère sont fréquemment traités par des inhibiteurs sélectifs de la recapture de la sérotonine (SSRI). Cependant, seulement 30-50% des patients répondront à ce type de traitement. Actuellement, il n'existe pas de marqueur biologique utilisable pour prédire la réponse à un traitement par SSRI. Un délai dans la mise en place d'une thérapie efficace peut avoir comme conséquences néfastes une augmentation du risque de suicide et une association avec un moins bon pronostic à long terme lors d'épisodes ultérieurs. Objectif : Par l'étude du métabolisme cérébral par tomographie par émission de positons (PET) au F-18-fluorodeoxyglucose (FDG), nous étudierons la présence de corrélations éventuelles entre la réponse clinique, qui généralement survient dans les 4 à 6 semaines après l'instauration du traitement antidépresseur, et une modification du métabolisme cérébral mesuré plus précocement, dans le but d'identifier les futurs répondeurs au traitement par SSRI. Méthodes : Cette étude longitudinale comprendra 20 patients unipolaires avec un épisode dépressif sévère au bénéfice d'un traitement par SSRI. Chacun des patients aura deux examens PET cérébraux au F-18-FDG. Le premier PET aura lieu juste avant le début du traitement aux SSRI et le second dans la 3ème semaine après début du traitement. La réponse clinique sera mesurée à 3 mois, et les répondeurs seront identifiés par une diminution significative des scores lors d'évaluation sur échelles de dépression. La recherche d'altérations métaboliques cérébrales sera faite en évaluant: (1) l'examen de base ou (2) l'examen PET précoce, à la recherche d'altérations spécifiques corrélées à une bonne réponse clinique, afin d'obtenir une valeur pronostique quant à la réponse au traitement. L'analyse de l'imagerie cérébrale utilisera la technique SPM (Statistical Parameter Mapping) impliquant un traitement numérique voxel par voxel des images PET. Résultats escomptés : Cette étude caractérisant les variations du métabolisme cérébral dans la phase précoce d'un traitement par SSRI vise à identifier des marqueurs métaboliques potentiels fournissant une valeur prédictive quant à la future efficacité du traitement SSRI introduit. Plus-value escomptée : L'identification d'un tel marqueur métabolique permettrait d'identifier rapidement les futurs répondeurs aux SSRI, et par conséquent d'éviter de proposer aux non-répondeurs la poursuite d'une médication, pendant plusieurs semaines, qui aurait peu de chance d'être efficace. Ainsi, une identification précoce des répondeurs aux SSRI pourrait permettre d'éviter des délais dans la mise en place d'une thérapie efficace et d'obtenir une amélioration du pronostic à plus long terme, avec une influence favorable sur les coûts de la santé.

Integrating postoperative value of cardiac Troponin I for a better correlation with in-hospital outcomes after congenital heart surgery.

Objective: Cardiac Troponin-I (cTnI) is a well-recognized early postoperative marker for myocardial damage in adults and children after heart surgery. The present study was undertaken to evaluate whether the integrated value (area under the curve(AUC)) of postoperative cTnI is a better mode to predict long-term outcome than post operative cTnI maximum value, after surgery for congenital heart defects (CHD). Methods: retrospective cohort study. 279 patients (mean age 4.6 years; range 0-17 years-old, 185 males) with congenital heart defect repair on cardiopulmonary by-pass were retrieved from our database including postoperative cTnI values. Maximal post operative cTnI value, post operative cTnI AUC value at 48h and total post operative cTnI AUC value were calculated and then correlated with duration of intubation, duration of ICU stay and mortality. Results: the mean duration of mechanical ventilation was 5.1+/-7.2 days and mean duration of ICU stay was 11.0+/- 13.3 days,11 patients (3.9%) died in post operative period. When comparing survivor and deceased groups, there was a significant difference in the mean value for max cTnI (16.7+/- 21.8 vs 59.2+/-41.4 mcg/l, p+0.0001), 48h AUC cTnI (82.0+/-110.7 vs 268.8+/-497.7 mcg/l, p+0.0001) and total AUC cTnI (623.8+/-1216.7 vs 2564+/-2826.0, p+0.0001). Analyses for duration of mechanical ventilation and duration of ICU stay by linear regression demonstrated a better correlation for 48h AUC cTnI (ventilation time r+0.82, p+0.0001 and ICU stay r+0.74, p+0.0001) then total AUC cTnI (ventilation time r+0.65, p+0.0001 and ICU stay r+0.60, p+0.0001) and max cTnI (ventilation time r+0.64, p+0.0001 and ICU stay r+0.60, p+0.0001). Conclusion: Cardiac Troponin I is a specific and sensitive marker of myocardial injury after congenital heart surgery and it may predict early in-hospital outcomes. Integration of post operative value of cTnI by calculation of AUC improves prediction of early in-hospital outcomes. It probably takes into account, not only the initial surgical procedure, but probably also incorporates the occurrence of hypoxic-ischemic phenomena in the post-operative period.

The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.

Long-term prognostic value of single-photon emission computed tomography myocardial perfusion imaging in diabetic patients with and without coronary artery disease

Keywords Diabetes mellitus; coronary artery disease; myocardial ischemia; prognostic value; single-photon emission computed tomography myocardial perfusion imaging Summary Aim: To determine the long-term prognostic value of SPECT myocardial perfusion imaging (MPI) for the occurrence of cardiovascular events in diabetic patients. Methods: SPECT MPI of 210 consecutive Caucasian diabetic patients were analysed using Kaplan-Meier event-free survival curves and independent predictors were determined by Cox multivariate analyses. Results: Follow-up was complete in 200 (95%) patients with a median period of 3.0 years (0.8-5.0). The population was composed of 114 (57%) men, age 65±10 years, 181 (90.5%) type 2 diabetes mellitus, 50 (25%) with a history of coronary artery disease (CAD) and 98 (49%) presenting chest pain prior to MPI. The prevalence of abnormal MPI was 58%. Patients with a normal MPI had neither cardiac death, nor myocardial infarction, independently of a history of coronary artery disease or chest pain. Among the independent predictors of cardiac death and myocardial infarction, the strongest was abnormal MPI (p<.0001), followed by history of CAD (Hazard Ratio (HR)= t 5.9, p=0.0001), diabetic retinopathy (HR=10.0, p=0.001) and inability to exercise (HR=7.7, p=0.02). Patients with normal 1VIPI had a low revascularisation rate of 2.4% during the follow-up period. Compared to normal MPI, cardiovascular events increased 5.2 fold for reversible defects, 8.5 fold for fixed defects and 20.1 fold for the association of both defects. Conclusion: Diabetic patients with normal MPI had an excellent prognosis independently of history of CAD. On the opposite, an abnormal MPI led to a > 5 fold increase in cardiovascular events. This emphasizes the value of SPECT MPI in predicting and risk-stratifying cardiovascular events in diabetic patients. Mots-Clés Diabète; maladie coronarienne; ischémie myocardique; valeur pronostique; tomoscintigraphie myocardique de perfusion par émission monophotonique Résumé Objectifs: Déterminer la valeur pronostique à long terme de la tomoscintigraphie myocardique de perfusion (TSMP) chez les patients diabétiques pour prédire les événements cardiovasculaires (ECV). Méthodes: Etude de 210 diabétiques caucasiens consécutifs référés pour une TSMP. Les courbes de survie ont été déterminées par Kaplan-Meier et les facteurs prédictifs indépendants par analyses multivariées de type Cox. Résultats: Le suivi a été complet chez 200 (95%) patients avec une durée médiane de 3.0 ans (0.8-50). La population était composée de 114 (57%) hommes, âge moyen 65±10 ans, avec 181 (90.5%) diabète de type 2, 50 (25%) antécédents de maladie coronarienne (AMC) et 98 (49%) patients connus pour un angor avant la TSMP. La prévalence de TSMP anormales était de 58%. Aucun décès d'origine cardiaque ou infarctus du myocarde n'est survenu chez les patients avec une TSMP normale, ceci indépendamment de leurs AMC et des douleurs thoraciques. Les facteurs prédictifs indépendants pour les ECV sont une TSMP anormale (p<.0001), les AMC (Hazard Ratio (HR)=15.9, p-0.0001), suivi de la rétinopathie diabétique (HR-10.0, p=0.001) et de l'incapacité à effectuer un exercice (HR=7.7, p=0.02). Les patients avec une TSMP normale ont présenté un taux de revascularisations de 2.4%. La présence de défauts mixtes accroît le risque d'ECV de 20.1 fois, les défauts fixes de 8.5 fois et les défauts réversibles de 5.2 fois comparés aux sujets avec une TSMP normale. Conclusion: Les patients diabétiques, coronariens ou non, avec une tomoscintigraphie myocardique de perfusion normale ont un excellent pronostique. A l'opposé, une TSMP anormale est associée à une augmentation du risque d'ECV de plus de 5 fois. Ceci confirme l'utilité de la TSMP dans la stratification du risque chez les patients diabétiques.

La cohorte OstéoLaus: évaluation des outils de routine clinique pour la prédiction de la fracture ostéoporotique [OsteoLaus: prediction of osteoporotic fractures by clinical risk factors and DXA, IVA and TBS].

OsteoLaus is a cohort of 1400 women 50 to 80 years living in Lausanne, Switzerland. Clinical risk factors for osteoporosis, bone ultrasound of the heel, lumbar spine and hip bone mineral density (BMD), assessment of vertebral fracture by DXA, and microarchitecture evaluation by TBS (Trabecular Bone Score) will be recorded. TBS is a new parameter obtained after a re-analysis of a DXA exam. TBS is correlated with parameters of microarchitecture. His reproducibility is good. TBS give an added diagnostic value to BMD, and predict osteoporotic fracture (partially) independently to BMD. The position of TBS in clinical routine in complement to BMD and clinical risk factors will be evaluated in the OsteoLaus cohort.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

The predictive value of psychological assessment of candidates for gastric bypass: A medical chart review

Background and Objectives: Guidelines for bariatric surgery demand a psychological evaluation of applicants. The aim of this study was to evaluate if the presence of "psychological risk factors" predicts postoperative weight loss after gastric bypass. Methods: Medical records of obese women who underwent bariatric surgery between 2000 and 2004 were reviewed. Psychological assessment consisted of a one-hour semi-structured interview, summarized in a written report. Anthropometric assessment at baseline and 6,12,18 and 24 months after surgery included body weight, height and body mass index. Results: The mean BMI of included patients (N = 92) was 46.2 + 6,3 kg/m(2) (range 38.4-69.7). Based on the psychological assessment, 27% (N = 25) of the patients were classified as having "psychological risk factors" and 28% (N = 26) were diagnosed with a psychiatric diagnosis, most often major depression. Two years after gastric bypass, 16% of patients with "psychological risk factors" achieved an excellent result (%EWL > 75) versus 39% of those without (p < 0.05). About 1 out of 4 patients was in postoperative psychiatric treatment, but only half of them were identified as having "psychological risk factors" at baseline. Weight loss of patients initiating a psychiatric treatment only after surgery was less than of patients who continued psychiatric treatment already initiated before surgery (55.7 + 14.8 versus 66.5 + 14.2 %EWL). Conclusions: A single semi-structured psychological interview may identify patients who are at risk for diminished postoperative weight loss; however, psychological assessment did not identify those patients who were in need of a psychiatric postoperative treatment.

Impact of obesity-related genes in Spanish population

Background: The objective was to investigate the association between BMI and single nucleotide polymorphisms previously identified of obesity-related genes in two Spanish populations. Forty SNPs in 23 obesity-related genes were evaluated in a rural population characterized by a high prevalence of obesity (869 subjects, mean age 46 yr, 62% women, 36% obese) and in an urban population (1425 subjects, mean age 54 yr, 50% women, 19% obese). Genotyping was assessed by using SNPlex and PLINK for the association analysis. Results: Polymorphisms of the FTO were significantly associated with BMI, in the rural population (beta 0.87, p-value <0.001). None of the other SNPs showed significant association after Bonferroni correction in the two populations or in the pooled analysis. A weighted genetic risk score (wGRS) was constructed using the risk alleles of the Tag-SNPs with a positive Beta parameter in both populations. From the first to the fifth quintile of the score, the BMI increased 0.45 kg/m2 in Hortega and 2.0 kg/m2 in Pizarra. Overall, the obesity predictive value was low (less than 1%). Conclusion: The risk associated with polymorphisms is low and the overall effect on BMI or obesity prediction is minimal. A weighted genetic risk score based on genes mainly acting through central nervous system mechanisms was associated with BMI but it yields minimal clinical prediction for the obesity risk in the general population.

Estimating Gross Value Added (GVA) across multiple scales: theoretical approach and empirical validation

This paper presents an application of the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) approach to the estimation of quantities of Gross Value Added (GVA) referring to economic entities defined at different scales of study. The method first estimates benchmark values of the pace of GVA generation per hour of labour across economic sectors. These values are estimated as intensive variables –e.g. €/hour– by dividing the various sectorial GVA of the country (expressed in € per year) by the hours of paid work in that same sector per year. This assessment is obtained using data referring to national statistics (top down information referring to the national level). Then, the approach uses bottom-up information (the number of hours of paid work in the various economic sectors of an economic entity –e.g. a city or a province– operating within the country) to estimate the amount of GVA produced by that entity. This estimate is obtained by multiplying the number of hours of work in each sector in the economic entity by the benchmark value of GVA generation per hour of work of that particular sector (national average). This method is applied and tested on two different socio-economic systems: (i) Catalonia (considered level n) and Barcelona (considered level n-1); and (ii) the region of Lima (considered level n) and Lima Metropolitan Area (considered level n-1). In both cases, the GVA per year of the local economic entity –Barcelona and Lima Metropolitan Area – is estimated and the resulting value is compared with GVA data provided by statistical offices. The empirical analysis seems to validate the approach, even though the case of Lima Metropolitan Area indicates a need for additional care when dealing with the estimate of GVA in primary sectors (agriculture and mining).

Estimation of the real population and its impact on the utilisation of healthcare services in Mediterranean resort regions: an ecological study.

BACKGROUND The demographic structure has a significant influence on the use of healthcare services, as does the size of the population denominators. Very few studies have been published on methods for estimating the real population such as tourist resorts. The lack of information about these problems means there is a corresponding lack of information about the behaviour of populational denominators (the floating population or tourist load) and the effect of this on the use of healthcare services. The objectives of the study were: a) To determine the Municipal Solid Waste (MSW) ratio, per person per day, among populations of known size; b) to estimate, by means of this ratio, the real population in an area where tourist numbers are very significant; and c) to determine the impact on the utilisation of hospital emergency healthcare services of the registered population, in comparison to the non-resident population, in two areas where tourist numbers are very significant. METHODS An ecological study design was employed. We analysed the Healthcare Districts of the Costa del Sol and the island of Menorca. Both are Spanish territories in the Mediterranean region. RESULTS In the two areas analysed, the correlation coefficient between the MSW ratio and admissions to hospital emergency departments exceeded 0.9, with p < 0.001. On the basis of MSW generation ratios, obtained for a control zone and also measured in neighbouring countries, we estimated the real population. For the summer months, when tourist activity is greatest and demand for emergency healthcare at hospitals is highest, this value was found to be double that of the registered population. CONCLUSION The MSW indicator, which is both ecological and indirect, can be used to estimate the real population in areas where population levels vary significantly during the year. This parameter is of interest in planning and dimensioning the provision of healthcare services.