A comparative study of dyslipidaemia in men and woman with androgenic alopecia

Several studies have analyzed the relationship between androgenetic alopecia and cardiovascular disease (mainly heart disease). However few studies have analyzed lipid values in men and women separately. This case-control study included 300 patients consecutively admitted to an outpatient clinic, 150 with early onset androgenetic alopecia (80 males and 70 females) and 150 controls (80 males and 70 females) with other skin diseases. Female patients with androgenic alopecia showed significant higher triglycerides values (123.8 vs 89.43 mg/dl, p = 0.006), total cholesterol values (196.1 vs 182.3 mg/dl, p = 0.014), LDL-C values (114.1 vs 98.8 mg/dl, p = 0.0006) and lower HDL-C values (56.8 vs 67.7 mg/dl, p <0.0001) versus controls respectively. Men with androgenic alopecia showed significant higher triglycerides values (159.7 vs 128.7 mg/dl, p = 0.04) total cholesterol values (198.3 vs 181.4 mg/dl, p = 0.006) and LDL-C values (124.3 vs 106.2, p = 0.0013) versus non-alopecic men. A higher prevalence of dyslipidemia in women and men with androgenic alopecia has been found. The elevated lipid values in these patients may contribute, alongside other mechanisms, to the development of cardiovascular disease in patient with androgenic alopecia.

CD209 in inflammatory bowel disease: a case-control study in the Spanish population

BACKGROUND The etiology of Ulcerative Colitis (UC) and Crohn's Disease (CD), considered together as Inflammatory Bowel Diseases (IBD), involves environmental and genetic factors. Although some genes are already known, the genetics underlying these diseases is complex and new candidates are continuously emerging. The CD209 gene is located in a region linked previously to IBD and a CD209 functional polymorphism (rs4804803) has been associated to other inflammatory conditions. Our aim was to study the potential involvement of this CD209 variant in IBD susceptibility. METHODS We performed a case-control study with 515 CD patients, 497 UC patients and 731 healthy controls, all of them white Spaniards. Samples were typed for the CD209 single nucleotide polymorphism (SNP) rs4804803 by TaqMan technology. Frequency comparisons were performed using chi2 tests. RESULTS No association between CD209 and UC or CD was observed initially. However, stratification of UC patients by HLA-DR3 status, a strong protective allele, showed that carriage of the CD209_G allele could increase susceptibility in the subgroup of HLA-DR3-positive individuals (p = 0.03 OR = 1.77 95% CI 1.04-3.02, vs. controls). CONCLUSION A functional variant in the CD209 gene, rs4804803, does not seem to be influencing Crohn's disease susceptibility. However, it could be involved in the etiology or pathology of Ulcerative Colitis in HLA-DR3-positive individuals but further studies are necessary.

Influencia de la temperatura en la ingesta de pacientes hospitalizados.

BACKGROUND AND OBJECTIVES Prevalence of hyponutrition in hospitalized patients is very high and it has been shown to be an important prognostic factor. Most of admitted patients depend on hospital food to cover their nutritional demands being important to assess the factors influencing their intake, which may be modified in order to improve it and prevent the consequences of inadequate feeding. In previous works, it has been shown that one of the worst scored characteristics of dishes was the temperature. The aim of this study was to assess the influence of temperature on patient's satisfaction and amount eaten depending on whether the food was served in isothermal trolleys keeping proper food temperature or not. MATERIAL AND METHODS We carried out satisfaction surveys to hospitalized patients having regular diets, served with or without isothermal trolleys. The following data were gathered: age, gender, weight, number of visits, mobility, autonomy, amount of orally taken medication, intake of out-of-hospital foods, qualification of food temperature, presentation and smokiness, amount of food eaten, and reasons for not eating all the content of the tray. RESULTS Of the 363 surveys, 134 (37.96%) were done to patients with isothermal trays and 229 (62.04%) to patients without them. Sixty percent of the patients referred having eaten less than the normal amount within the last week, the most frequent reason being decreased appetite. During lunch and dinner, 69.3% and 67.7%, respectively, ate half or less of the tray content, the main reasons being as follows: lack of appetite (42% at lunch time and 40% at dinner), do not like the food (24.3 and 26.2%) or taste (15.3 and 16.8%). Other less common reasons were the odor, the amount of food, having nausea or vomiting, fatigue, and lack of autonomy. There were no significant differences in the amount eaten by gender, weight, number of visits, amount of medication, and level of physical activity. The food temperature was classified as adequate by 62% of the patients, the presentation by 95%, and smokiness by 85%. When comparing the patients served with or without isothermal trays, there were no differences with regards to baseline characteristics analyzed that might have had an influence on amount eaten. Ninety percent of the patients with isothermal trolley rated the food temperature as good, as compared with 57.2% of the patients with conventional trolley, the difference being statistically significant (P = 0.000). Besides, there were differences in the amount of food eaten between patients with and without isothermal trolley, so that 41% and 27.7% ate all the tray content, respectively, difference being statistically significant (P = 0.007). There were no differences in smokiness or presentation rating. CONCLUSIONS Most of the patients (60%) had decreased appetite during hospital admission. The percentage of hospitalized patients rating the food temperature as being good is higher among patients served with isothermal trolleys. The amount of food eaten by the patients served with isothermal trolleys is significantly higher that in those without them.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Proceso INFORNUT: validación de la fase de filtro-FILNUT--y comparación con otros métodos de detección precoz de desnutrición hospitalaria.

INTRODUCTION According to several series, hospital hyponutrition involves 30-50% of hospitalized patients. The high prevalence justifies the need for early detection from admission. There several classical screening tools that show important limitations in their systematic application in daily clinical practice. OBJECTIVES To analyze the relationship between hyponutrition, detected by our screening method, and mortality, hospital stay, or re-admissions. To analyze, as well, the relationship between hyponutrition and prescription of nutritional support. To compare different nutritional screening methods at admission on a random sample of hospitalized patients. Validation of the INFORNUT method for nutritional screening. MATERIAL AND METHODS In a previous phase from the study design, a retrospective analysis with data from the year 2003 was carried out in order to know the situation of hyponutrition in Virgen de la Victoria Hospital, at Malaga, gathering data from the MBDS (Minimal Basic Data Set), laboratory analysis of nutritional risk (FILNUT filter), and prescription of nutritional support. In the experimental phase, a cross-sectional cohort study was done with a random sample of 255 patients, on May of 2004. Anthropometrical study, Subjective Global Assessment (SGA), Mini-Nutritional Assessment (MNA), Nutritional Risk Screening (NRS), Gassull's method, CONUT and INFORNUT were done. The settings of the INFORNUT filter were: albumin < 3.5 g/dL, and/or total proteins <5 g/dL, and/or prealbumin <18 mg/dL, with or without total lymphocyte count < 1.600 cells/mm3 and/or total cholesterol <180 mg/dL. In order to compare the different methods, a gold standard is created based on the recommendations of the SENPE on anthropometrical and laboratory data. The statistical association analysis was done by the chi-squared test (a: 0.05) and agreement by the k index. RESULTS In the study performed in the previous phase, it is observed that the prevalence of hospital hyponutrition is 53.9%. One thousand six hundred and forty four patients received nutritional support, of which 66.9% suffered from hyponutrition. We also observed that hyponutrition is one of the factors favoring the increase in mortality (hyponourished patients 15.19% vs. non-hyponourished 2.58%), hospital stay (hyponourished patients 20.95 days vs. non-hyponourished 8.75 days), and re-admissions (hyponourished patients 14.30% vs. non-hyponourished 6%). The results from the experimental study are as follows: the prevalence of hyponutrition obtained by the gold standard was 61%, INFORNUT 60%. Agreement levels between INFORNUT, CONUT, and GASSULL are good or very good between them (k: 0.67 INFORNUT with CONUT, and k: 0.94 INFORNUT and GASSULL) and wit the gold standard (k: 0.83; k: 0.64 CONUT; k: 0.89 GASSULL). However, structured tests (SGA, MNA, NRS) show low agreement indexes with the gold standard and laboratory or mixed tests (Gassull), although they show a low to intermediate level of agreement when compared one to each other (k: 0.489 NRS with SGA). INFORNUT shows sensitivity of 92.3%, a positive predictive value of 94.1%, and specificity of 91.2%. After the filer phase, a preliminary report is sent, on which anthropometrical and intake data are added and a Nutritional Risk Report is done. CONCLUSIONS Hyponutrition prevalence in our study (60%) is similar to that found by other authors. Hyponutrition is associated to increased mortality, hospital stay, and re-admission rate. There are no tools that have proven to be effective to show early hyponutrition at the hospital setting without important applicability limitations. FILNUT, as the first phase of the filter process of INFORNUT represents a valid tool: it has sensitivity and specificity for nutritional screening at admission. The main advantages of the process would be early detection of patients with risk for hyponutrition, having a teaching and sensitization function to health care staff implicating them in nutritional assessment of their patients, and doing a hyponutrition diagnosis and nutritional support need in the discharge report that would be registered by the Clinical Documentation Department. Therefore, INFORNUT would be a universal screening method with a good cost-effectiveness ratio.

Caracterització estadística dels pics de correlació a GPS

Aquest projecte es centra a donar una caracterització estadística del valor que tenen els pics de correlació sota diferents escenaris d’adquisició a un receptor GPS. En primer lloc, s’ha volgut donar una visió general de tots els fonaments del sistema GPS per tal de poder entendre el seu funcionament. A continuació, s’ha passat a analitzar el bloc d’adquisició d’un receptor GPS. Primer, hem estudiat quines operacions es realitzen en aquest bloc i quines són les diferents formes d’implementar-lo. Seguidament, sota un escenari d’adquisició per cerca de fase de codi en paral·lel i utilitzant integracions coherents, s’han estudiat les distribucions estadístiques de les pdf’s obtingudes pels pics de correlació de senyal+soroll i pels pics de correlació de només soroll, i s’ha vist com aquestes fan modificar la corba ROC del receptor . Les simulacions s’han realitzat amb MATLAB i en diferents escenaris d’adquisició per tal de poder comparar com varien les estadístiques obtingudes en casos diferents.

Macronutrient composition of the diet and prospective weight change in participants of the EPIC-PANACEA study.

BACKGROUND The effect of the macronutrient composition of the usual diet on long term weight maintenance remains controversial. METHODS 373,803 subjects aged 25-70 years were recruited in 10 European countries (1992-2000) in the PANACEA project of the EPIC cohort. Diet was assessed at baseline using country-specific validated questionnaires and weight and height were measured at baseline and self-reported at follow-up in most centers. The association between weight change after 5 years of follow-up and the iso-energetic replacement of 5% of energy from one macronutrient by 5% of energy from another macronutrient was assessed using multivariate linear mixed-models. The risk of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to initial Body Mass Index. RESULTS A higher proportion of energy from fat at the expense of carbohydrates was not significantly associated with weight change after 5 years. However, a higher proportion of energy from protein at the expense of fat was positively associated with weight gain. A higher proportion of energy from protein at the expense of carbohydrates was also positively associated with weight gain, especially when carbohydrates were rich in fibre. The association between percentage of energy from protein and weight change was slightly stronger in overweight participants, former smokers, participants ≥60 years old, participants underreporting their energy intake and participants with a prudent dietary pattern. Compared to diets with no more than 14% of energy from protein, diets with more than 22% of energy from protein were associated with a 23-24% higher risk of becoming overweight or obese in normal weight and overweight subjects at baseline. CONCLUSION Our results show that participants consuming an amount of protein above the protein intake recommended by the American Diabetes Association may experience a higher risk of becoming overweight or obese during adult life.

Protocolo de investigación del atlas de mortalidad de las capitales de provincia de Andalucía y Cataluña (Proyecto AMCAC).

The aim of this work is to make known the multicentric project AMCAC, whose objective is to describe the geographical distribution of mortality from all causes in census groups of the provincial capitals of Andalusia and Catalonia during 1992-2002 and 1994-2000 respectively, and to study the relationship between the sociodemographic characteristics of the census groups and mortality. This is an ecological study in which the analytical unit is the census group. The data correspond to 298,731 individuals (152,913 men and 145,818 women) who died during the study periods in the towns of Almeria, Barcelona, Cadiz, Cordoba, Girona, Granada, Huelva, Jaen, Lleida, Malaga, Seville and Tarragona during the study periods. The dependent variable is the number of deaths observed per census group. The independent variables are the percentage of unemployment, illiteracy and manual workers. Estimation of the moderated relative risk and the study of the associations among the sociodemographic characteristics of the census groups and the mortality will be done for each town and each sex using the Besag-York-Mollie model. Dissemination of the results will help to improve and broaden knowledge about the population's health, and will provide an important starting point to establish the influence of contextual variables on the health of urban populations.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Studying Avalanches in the ground-state of the two-dimensional random field Ising model driven by an external field

We study the exact ground state of the two-dimensional random-field Ising model as a function of both the external applied field B and the standard deviation ¿ of the Gaussian random-field distribution. The equilibrium evolution of the magnetization consists in a sequence of discrete jumps. These are very similar to the avalanche behavior found in the out-of-equilibrium version of the same model with local relaxation dynamics. We compare the statistical distributions of magnetization jumps and find that both exhibit power-law behavior for the same value of ¿. The corresponding exponents are compared.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Steganalytic methods for the detection of histogram shifting data-hiding schemes

In this paper, some steganalytic techniques designed to detect the existence of hidden messages using histogram shifting methods are presented. Firstly, some techniques to identify specific methods of histogram shifting, based on visible marks on the histogram or abnormal statistical distributions are suggested. Then, we present a general technique capable of detecting all histogram shifting techniques analyzed. This technique is based on the effect of histogram shifting methods on the "volatility" of the histogram of differences and the study of its reduction whenever new data are hidden.

Steganalytic Methods for the Detection of Histogram Shifting Data Hiding Schemes

Peer-reviewed

Affinity distribution functions in multicomponent heterogeneous adsorption. Analytical inversion of isotherms to obtain affinity spectra

An analytical approach for the interpretation of multicomponent heterogeneous adsorption or complexation isotherms in terms of multidimensional affinity spectra is presented. Fourier transform, applied to analyze the corresponding integral equation, leads to an inversion formula which allows the computation of the multicomponent affinity spectrum underlying a given competitive isotherm. Although a different mathematical methodology is used, this procedure can be seen as the extension to multicomponent systems of the classical Sips’s work devoted to monocomponent systems. Furthermore, a methodology which yields analytical expressions for the main statistical properties (mean free energies of binding and covariance matrix) of multidimensional affinity spectra is reported. Thus, the level of binding correlation between the different components can be quantified. It has to be highlighted that the reported methodology does not require the knowledge of the affinity spectrum to calculate the means, variances, and covariance of the binding energies of the different components. Nonideal competitive consistent adsorption isotherm, widely used in metal/proton competitive complexation to environmental macromolecules, and Frumkin competitive isotherms are selected to illustrate the application of the reported results. Explicit analytical expressions for the affinity spectrum as well as for the matrix correlation are obtained for the NICCA case. © 2004 American Institute of Physics.

Characteristics of 2D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS

Flood simulation studies use spatial-temporal rainfall data input into distributed hydrological models. A correct description of rainfall in space and in time contributes to improvements on hydrological modelling and design. This work is focused on the analysis of 2-D convective structures (rain cells), whose contribution is especially significant in most flood events. The objective of this paper is to provide statistical descriptors and distribution functions for convective structure characteristics of precipitation systems producing floods in Catalonia (NE Spain). To achieve this purpose heavy rainfall events recorded between 1996 and 2000 have been analysed. By means of weather radar, and applying 2-D radar algorithms a distinction between convective and stratiform precipitation is made. These data are introduced and analyzed with a GIS. In a first step different groups of connected pixels with convective precipitation are identified. Only convective structures with an area greater than 32 km2 are selected. Then, geometric characteristics (area, perimeter, orientation and dimensions of the ellipse), and rainfall statistics (maximum, mean, minimum, range, standard deviation, and sum) of these structures are obtained and stored in a database. Finally, descriptive statistics for selected characteristics are calculated and statistical distributions are fitted to the observed frequency distributions. Statistical analyses reveal that the Generalized Pareto distribution for the area and the Generalized Extreme Value distribution for the perimeter, dimensions, orientation and mean areal precipitation are the statistical distributions that best fit the observed ones of these parameters. The statistical descriptors and the probability distribution functions obtained are of direct use as an input in spatial rainfall generators.