Prevalence of frailty indicators and association with socioeconomic status in middle-aged and older adults in a swiss region with universal health insurance coverage : a population-based cross-sectional study

Frailty prevalence in older adults has been reported but is largely unknown in middle-aged adults. We determined the prevalence of frailty indicators among middle-aged and older adults from a general Swiss population characterized by universal health insurance coverage and assessed the determinants of frailty with a special focus on socioeconomic status. Participants aged 50 and more from the population-based 2006-2010 Bus Santé study were included (N = 2,930). Four frailty indicators (weakness, shrinking, exhaustion, and low activity) were measured according to standard definitions. Multivariate logistic regressions were used to determine associations. Overall, 63.5%, 28.7%, and 7.8% participants presented no frailty indicators, one frailty indicator, and two or more frailty indicators, respectively. Among middle-aged participants (50-65 years), 75.1%, 22.2%, and 2.7% presented 0, 1, and 2 or more frailty indicators. The number of frailty indicators was positively associated with age, hypertension, and current smoking and negatively associated with male gender, body mass index, waist-to-hip ratio, and serum total cholesterol level. Lower income level but not education was associated with higher number of frailty indicators. Frailty indicators are frequently encountered in both older and middle-aged adults from the Swiss general population. Despite universal health insurance coverage, household income is independently associated with frailty.

Species identification in mammals from mixed biological samples based on mitochondrial DNA control region length polymorphism.

The ability to identify the species origin of an unknown biological sample is relevant in the fields of human and wildlife forensics. However, the detection of several species mixed in the same sample still remains a challenge. We developed and tested a new approach for mammal DNA identification in mixtures of two or three species, based on the analysis of mitochondrial DNA control region interspecific length polymorphism followed by direct sequencing. Contrary to other published methods dealing with species mixtures, our protocol requires a single universal primer pair and is not based on a pre-defined panel of species. Amplicons can be separated either on agarose gels or using CE. The advantages and limitations of the assay are discussed under different conditions, such as variable template concentration, amplicon sizes and size difference among the amplicons present in the mixture. For the first time, this protocol provides a simple, reliable and flexible method for simultaneous identification of multiple mammalian species from mixtures, without any prior knowledge of the species involved.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

The identification of two Trypanosoma cruzi I genotypes from domestic and sylvatic transmission cycles in Colombia based on a single polymerase chain reaction amplification of the spliced-leader intergenic region

A single polymerase chain reaction (PCR) reaction targeting the spliced-leader intergenic region of Trypanosoma cruzi I was standardised by amplifying a 231 bp fragment in domestic (TcIDOM) strains or clones and 450 and 550 bp fragments in sylvatic strains or clones. This reaction was validated using 44 blind coded samples and 184 non-coded T. cruzi I clones isolated from sylvatic triatomines and the correspondence between the amplified fragments and their domestic or sylvatic origin was determined. Six of the nine strains isolated from acute cases suspected of oral infection had the sylvatic T. cruzi I profile. These results confirmed that the sylvatic T. cruzi I genotype is linked to cases of oral Chagas disease in Colombia. We therefore propose the use of this novel PCR reaction in strains or clones previously characterised as T. cruziI to distinguish TcIDOMfrom sylvatic genotypes in studies of transmission dynamics, including the verification of population selection within hosts or detection of the frequency of mixed infections by both T. cruzi I genotypes in Colombia.

DNA prime-protein boost based vaccination with a conserved region of leptospiral immunoglobulin-like A and B proteins enhances protection against leptospirosis

Leptospirosis is a zoonotic disease caused by pathogenic spirochetes of theLeptospira genus. Vaccination with bacterins has severe limitations. Here, we evaluated the N-terminal region of the leptospiral immunoglobulin-like B protein (LigBrep) as a vaccine candidate against leptospirosis using immunisation strategies based on DNA prime-protein boost, DNA vaccine, and subunit vaccine. Upon challenge with a virulent strain ofLeptospira interrogans, the prime-boost and DNA vaccine approaches induced significant protection in hamsters, as well as a specific IgG antibody response and sterilising immunity. Although vaccination with recombinant fragment of LigBrep also produced a strong antibody response, it was not immunoprotective. These results highlight the potential of LigBrep as a candidate antigen for an effective vaccine against leptospirosis and emphasise the use of the DNA prime-protein boost as an important strategy for vaccine development.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

BACKGROUND: Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. METHODS: We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay. RESULTS: Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. CONCLUSIONS: IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Simultaneous identification of multiple mammalian species from mixed forensic samples based on mtDNA control region length polymorphism

Molecular species identification in mixed or contaminated biological material has always been problematic. We developed a simple and accurate method for mammal DNA identification in mixtures, based on interspecific mitochondrial DNA control region length polymorphism. Contrary to other published methods dealing with species mixtures, our protocol requires a single universal primer pair and amplification step, and is not based on a pre-defined panel of species. This protocol has been routinely employed by our laboratory for species identification in dozens of human and animal forensic caseworks. Six representative forensic caseworks involving the specific identification of mixed animal samples are reported in this paper, in order to demonstrate the applicability and usefulness of the method.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Methadone treatments in a Swiss Region, 2001-2008: a registry-based analysis.

ABSTRACT: BACKGROUND: To determine, in a region of Switzerland, the duration of retention in opioid substitution treatments with methadone (OSTM), duration of treatment interruptions, probability of re-entry to treatment after a treatment interruption, and associated factors. METHODS: A secondary analysis of registry-based data was performed with patients (n = 2880) registered in the methadone treatment register database of the Public Health Service of the canton of Vaud between January 1, 2001 and June 30, 2008. Survival analysis and multivariate analysis was conducted. RESULTS: The probability of remaining on treatment was 69% at 1 year and 45% at 3 years (n =1666). One-third of patients remained on treatment beyond 5 years. The estimated hazard of leaving treatment was increased by a ratio of 1.31 in the case of a first treatment (P = 0.001), 1.83 for those without a fixed home (P < 0.001), and 1.29 for those younger than 30 years old (P < 0.001). The probability of having begun a new treatment after a first interruption was 21% at one year, 38% at 3 years, and 43% at 5 years (n = 1581). Factors at the interruption of treatment associated with a higher probability of re-entering were: interruption not due to methadone withdrawal, bad physical health, and higher methadone dose. CONCLUSIONS: OSTM are long-term (maintenance) treatments in Switzerland. Younger age, bad living conditions at entry, and first treatment are predictors of lower retention. Approximately one-half of patients who interrupt treatment will re-enter treatment within 5 years.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

High prevalence of forgoing healthcare for economic reasons in Switzerland: a population-based study in a region with universal health insurance coverage.

OBJECTIVE: To investigate the determinants and the 4-year evolution of the forgoing of healthcare for economic reasons in Switzerland. METHOD: Population-based survey (2007-2010) of a representative sample aged 35-74years in the Canton of Geneva, Switzerland. Healthcare forgone, socioeconomic and insurance status, marital status, and presence of dependent children were assessed using standardized methods. RESULTS: A total of 2601 subjects were included in the analyses. Of the subjects, 13.8% (358/2601) reported having forgone healthcare for economic reasons, with the percentage varying from 3.7% in the group with a monthly income ≥13,000CHF (1CHF≈1$) to 30.9% in the group with a monthly income <3000CHF. In subjects with a monthly income <3000CHF, the percentage who had forgone healthcare increased from 22.5% in 2007/8 to 34.7% in 2010 (P trend=0.2). Forgoing healthcare for economic reasons was associated with lower income, female gender, smoking status, lower job position, having dependent children, being divorced and single, paying a higher deductible, and receiving a premium subsidy. CONCLUSION: In a Swiss region with universal health insurance coverage, the reported prevalence of forgoing healthcare for economic reasons was high and greatly dependent on socioeconomic factors. Our data suggested an increasing trend among participants with the lowest income.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed