Hepatitis C prevalence and risk factors in hemodialysis patients in Central Brazil: a survey by polymerase chain reaction and serological methods

An hemodialysis population in Central Brazil was screened by polymerase chain reaction (PCR) and serological methods to assess the prevalence of hepatitis C virus (HCV) infection and to investigate associated risk factors. All hemodialysis patients (n=428) were interviewed in eight dialysis units in Goiânia city. Blood samples were collected and serum samples screened for anti-HCV antibodies by an enzyme-linked immunosorbent assay (ELISA). Positive samples were retested for confirmation with a line immunoassay (LIA). All samples were also tested for HCV RNA by the PCR. An overall prevalence of 46.7% (CI 95%: 42-51.5) was found, ranging from 20.7% (CI 95%: 8.8-38.1) to 90.4% (CI 95%: 79.9-96.4) depending on the dialysis unit. Of the 428 patients, 185 were found to be seropositive by ELISA, and 167 were confirmed positive by LIA, resulting in an anti-HCV prevalence of 39%. A total of 131 patients were HCV RNA-positive. HCV viremia was present in 63.5% of the anti-HCV-positive patients and in 10.3% of the anti-HCV-negative patients. Univariate analysis of risk factors showed that the number of previous blood transfusions, transfusion of blood before mandatory screening for anti-HCV, length of time on hemodialysis, and treatment in multiple units were associated with HCV positivity. However, multivariate analysis revealed that blood transfusion before screening for anti-HCV and length of time on hemodialysis were significantly associated with HCV infection in this population. These data suggest that nosocomial transmission may play a role in the spread of HCV in the dialysis units studied. In addition to anti-HCV screening, HCV RNA detection is necessary for the diagnosis of HCV infection in hemodialysis patients.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

ALK rearrangement in a selected population of advanced non small cell lung cancer patients. FISH and inmunohistochemistry diagnostic methods, prevalence and clinical outcomes

Anaplastic lymphoma kinase (ALK) rearrangements represents a new driver oncogenic event in non-small cell lung cancer (NSCLC). ALK positive patients account for a 1-7% of NSCLC patients. The objective of this study is to know the prevalence and clinical characteristics of ALK positive patients in a cohort of NSCLC patients and to compare inmunohistochemistry with D5F3 monoclonal antibody with gold standard method fluorescence in situ hybridation

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Iterative reconstruction methods in two different MDCT scanners: Physical metrics and 4-alternative forced-choice detectability experiments - A phantom approach.

This paper characterizes and evaluates the potential of three commercial CT iterative reconstruction methods (ASIR?, VEO? and iDose(4 ()?())) for dose reduction and image quality improvement. We measured CT number accuracy, standard deviation (SD), noise power spectrum (NPS) and modulation transfer function (MTF) metrics on Catphan phantom images while five human observers performed four-alternative forced-choice (4AFC) experiments to assess the detectability of low- and high-contrast objects embedded in two pediatric phantoms. Results show that 40% and 100% ASIR as well as iDose(4) levels 3 and 6 do not affect CT number and strongly decrease image noise with relative SD constant in a large range of dose. However, while ASIR produces a shift of the NPS curve apex, less change is observed with iDose(4) with respect to FBP methods. With second-generation iterative reconstruction VEO, physical metrics are even further improved: SD decreased to 70.4% at 0.5 mGy and spatial resolution improved to 37% (MTF(50%)). 4AFC experiments show that few improvements in detection task performance are obtained with ASIR and iDose(4), whereas VEO makes excellent detections possible even at an ultra-low-dose (0.3 mGy), leading to a potential dose reduction of a factor 3 to 7 (67%-86%). In spite of its longer reconstruction time and the fact that clinical studies are still required to complete these results, VEO clearly confirms the tremendous potential of iterative reconstructions for dose reduction in CT and appears to be an important tool for patient follow-up, especially for pediatric patients where cumulative lifetime dose still remains high.

New nonculture-based methods for the diagnosis of invasive candidiasis.

PURPOSE OF REVIEW: Invasive candidiasis is a severe infectious complication occurring mostly in onco-hematologic and surgical patients. Its conventional diagnosis is insensitive and often late, leading to a delayed treatment and a high mortality. The purpose of this article is to review recent contributions in the nonconventional diagnostic approaches of invasive candidiasis, both for the detection of the epidose and the characterization of the etiologic agent. RECENT FINDINGS: Antigen-based tests to detect invasive candidiasis comprise a specific test, mannan, as well as a nonspecific test, beta-D-glucan. Both have a moderate sensitivity and a high specificity, and cannot be recommended alone as a negative screening tool or a positive syndrome driven diagnostic tool. Molecular-based tests still have not reached the stage of rapid, easy to use, standardized tests ideally complementing blood culture at the time of blood sampling. New tests (fluorescence in-situ hybridization or mass spectrometry) significantly reduce the delay of identification of Candida at the species level in positive blood cultures, and should have a positive impact on earlier appropriate antifungal therapy and possibly on outcome. SUMMARY: Both antigen-based and molecular tests appear as promising new tools to complement and accelerate the conventional diagnosis of invasive candidiasis with an expected significant impact on earlier and more focused treatment and on prognosis.

First characterization of Candida albicans by random amplified polymorphic DNA method in Nicaragua and comparison of the diagnosis methods for vaginal candidiasis in Nicaraguan women

A total of 106 women with vaginitis in Nicaragua were studied. The positive rate for the identification of Candida species was 41% (44 positive cultures out of 106 women with vaginitis). The sensitivity of microscopic examination of wet mount with the potassium hydroxide (KOH) was 61% and 70% with Gram's stain when using the culture of vaginal fluid as gold standard for diagnosis of candidiasis. Among the 44 positives cultures, isolated species of yeast from vaginal swabs were C. albicans (59%), C. tropicalis (23%), C. glabrata (14%) and C. krusei (4%). This study reports the first characterization of 26 C. albicans stocks from Nicaragua by the random amplified polymorphic DNA method. The genetic analysis in this small C. albicans population showed the existence of linkage disequilibrium, which is consistent with the hypothesis that C. albicans undergoes a clonal propagation.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Vom Finanz- zum Wissenschaftsbetrug : Methode, den Irrungen in der medizinischen Literatur beizukommen [From financial to scientific fraud : Methods to detect discrepancies in the medical literature].

Fraud is as old as Mankind. There are an enormous number of historical documents which show the interaction between truth and untruth; therefore it is not really surprising that the prevalence of publication discrepancies is increasing. More surprising is that new cases especially in the medical field generate such a huge astonishment. In financial mathematics a statistical tool for detection of fraud is known which uses the knowledge of Newcomb and Benford regarding the distribution of natural numbers. This distribution is not equal and lower numbers are more likely to be detected compared to higher ones. In this investigation all numbers contained in the blinded abstracts of the 2009 annual meeting of the Swiss Society of Anesthesia and Resuscitation (SGAR) were recorded and analyzed regarding the distribution. A manipulated abstract was also included in the investigation. The χ(2)-test was used to determine statistical differences between expected and observed counts of numbers. There was also a faked abstract integrated in the investigation. A p<0.05 was considered significant. The distribution of the 1,800 numbers in the 77 submitted abstracts followed Benford's law. The manipulated abstract was detected by statistical means (difference in expected versus observed p<0.05). Statistics cannot prove whether the content is true or not but can give some serious hints to look into the details in such conspicuous material. These are the first results of a test for the distribution of numbers presented in medical research.

Comparison of Methods for Evaluation of the Bactericidal Activity of Copper-Sputtered Surfaces against Methicillin-Resistant Staphylococcus aureus.

Bacteria can survive on hospital textiles and surfaces, from which they can be disseminated, representing a source of health care-associated infections (HCAIs). Surfaces containing copper (Cu), which is known for its bactericidal properties, could be an efficient way to lower the burden of potential pathogens. The antimicrobial activity of Cu-sputtered polyester surfaces, obtained by direct-current magnetron sputtering (DCMS), against methicillin-resistant Staphylococcus aureus (MRSA) was tested. The Cu-polyester microstructure was characterized by high-resolution transmission electron microscopy to determine the microstructure of the Cu nanoparticles and by profilometry to assess the thickness of the layers. Sputtering at 300 mA for 160 s led to a Cu film thickness of 20 nm (100 Cu layers) containing 0.209% (wt/wt) polyester. The viability of MRSA strain ATCC 43300 on Cu-sputtered polyester was evaluated by four methods: (i) mechanical detachment, (ii) microcalorimetry, (iii) direct transfer onto plates, and (iv) stereomicroscopy. The low efficacy of mechanical detachment impeded bacterial viability estimations. Microcalorimetry provided only semiquantitative results. Direct transfer onto plates and stereomicroscopy seemed to be the most suitable methods to evaluate the bacterial inactivation potential of Cu-sputtered polyester surfaces, since they presented the least experimental bias. Cu-polyester samples sputtered for 160 s by DCMS were further tested against 10 clinical MRSA isolates and showed a high level of bactericidal activity, with a 4-log(10) reduction in the initial MRSA load (10(6) CFU) within 1 h. Cu-sputtered polyester surfaces might be of use to prevent the transmission of HCAI pathogens.

Schistosomiasis mansoni: follow-up of control program based on parasitologic and serologic methods in a Brazilian community of low endemicity

A field survey on schistosomiais was carried out in 1998, in the municipality of Pedro de Toledo, a low endemic area in the state of São Paulo, Brazil. According to the parasitologic Kato-Katz method, the prevalence rate was 1.6%, with an infection intensity of 40.9 eggs per gram of stool. By the immunofluorescence test (IFT) for detection of IgG and IgM antibodies in the serum, IgG-IFT and IgM-IFT, respectively, prevalence indices of 33.2% and 33.5% were observed. To assess the impact of the schistosomiasis control program in the area, parasitologic and serologic data obtained in 1998, analyzed according to the age, sex, and residence zone, were compared to previous data obtained in a epidemiologic study carried out in 1980, when prevalence indices were of 22.8% and 55.5%, respectively by Kato-Katz and IgG-IFT. A significant fall of the prevalence was observed, indicating that the control measures were effective. Nonetheless, residual transmission was observed, demonstrating the need for a joint effort to include new approaches for better understanding the real situation and improving the control of the disease in low endemic areas.

Use of molecular methods in identification of Candida Species and evaluation of fluconazole resistance

The aim of this study was to evaluate the use of one of the molecular typing methods such as PCR (polymerase chain reaction) following by RFLP (restriction fragment length polymorphism) analysis in the identification of Candida species and then to differentiate the identified azole susceptible and resistant Candida albicans strains by using AP-PCR (arbitrarily primed-polymerase chain reaction). The identification of Candida species by PCR and RFLP analysis was based on the size and primary structural variation of rDNA intergenic spacer regions (ITS). Forty-four clinical Candida isolates comprising 5 species were included to the study. The amplification products were digested individually with 3 different restriction enzymes: HaeIII, DdeI, and BfaI. All the isolates tested yielded the expected band patterns by PCR and RFLP analysis. The results obtained from this study demonstrate that Candida species can be differentiated as C. albicans and non-C. albicans strains only by using HaeIII restriction enzyme and BfaI maintains the differentiation of these non-C. albicans species. After identification Candida species with RFLP analysis, C. albicans strains were included to the AP-PCR test. By using AP-PCR, fluconazole susceptible and resistant strains were differentiated. Nine fluconazole susceptible and 24 fluconazole resistant C. albicans were included to the study. Fluconazole resistant strains had more bands when evaluating with the agarose gel electrophoresis but there were no specific discriminatory band patterns to warrant the differentiation of the resistance. The identification of Candida species with the amplification of intergenic spacer region and RFLP analysis is a practical, short, and a reliable method when comparing to the conventional time-consuming Candida species identification methods. The fluconazole susceptibility testing with AP-PCR seems to be a promising method but further studies must be performed for more specific results.