990 resultados para BIOLOGICAL DETECTION
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OBJECTIVES: To investigate the contribution of a real-time PCR assay for the detection of Treponema pallidum in various biological specimens with the secondary objective of comparing its value according to HIV status. METHODS: Prospective cohort of incident syphilis cases from three Swiss hospitals (Geneva and Bern University Hospitals, Outpatient Clinic for Dermatology of Triemli, Zurich) diagnosed between January 2006 and September 2008. A case-control study was nested into the cohort. Biological specimens (blood, lesion swab or urine) were taken at diagnosis (as clinical information) and analysed by real-time PCR using the T pallidum 47 kDa gene. RESULTS: 126 specimens were collected from 74 patients with primary (n = 26), secondary (n = 40) and latent (n = 8) syphilis. Among primary syphilis, sensitivity was 80% in lesion swabs, 28% in whole blood, 55% in serum and 29% in urine, whereas among secondary syphilis, it was 20%, 36%, 47% and 44%, respectively. Among secondary syphilis, plasma and cerebrospinal fluid were also tested and provided a sensitivity of 100% and 50%, respectively. The global sensitivity of T pallidum by PCR (irrespective of the compartment tested) was 65% during primary, 53% during secondary and null during latent syphilis. No difference regarding serology or PCR results was observed among HIV-infected patients. Specificity was 100%. CONCLUSIONS: Syphilis PCR provides better sensitivity in lesion swabs from primary syphilis and displays only moderate sensitivity in blood from primary and secondary syphilis. HIV status did not modify the internal validity of PCR for the diagnosis of primary or secondary syphilis.
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AIMS: Bacillus anthracis strains of various origins were analysed with the view to describe intrinsic and persistent structural components of the Bacillus collagen-like protein of anthracis glycoprotein associated anthrose containing tetrasaccharide in the exosporium. METHODS AND RESULTS: The tetrasaccharide consists of three rhamnose residues and an unique monosaccharide--anthrose. As anthrose was not found in spores of related strains of bacteria, we envisioned the detection of B. anthracis spores based on antibodies against anthrose-containing polysaccharides. Carbohydrate-protein conjugates containing the synthetic tetrasaccharide, an anthrose-rhamnose disaccharide or anthrose alone were employed to immunize mice. All three formulations were immunogenic and elicited IgG responses with different fine specificities. All sera and monoclonal antibodies derived from tetrasaccharide immunized mice cross-reacted not only with spore lysates of a panel of virulent B. anthracis strains, but also with some of the B. cereus strains tested. CONCLUSIONS: Our results demonstrate that antibodies to synthetic carbohydrates are useful tools for epitope analyses of complex carbohydrate antigens and for the detection of particular target structures in biological specimens. SIGNIFICANCE AND IMPACT OF THE STUDY: Although not strictly specific for B. anthracis spores, antibodies against the tetrasaccharide may have potential as immuno-capturing components for a highly sensitive spore detection system.
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Aquatic toxicology is facing the challenge to assess the impact of complex mixtures of compounds on diverse biological endpoints. So far, ecotoxicology focuses mainly on apical endpoints such as growth, lethality and reproduction, but does not consider sublethal toxic effects that may indirectly cause ecological effects. One such sublethal effect is toxicant-induced impairment of neurosensory functions which will affect important behavioural traits of exposed organisms. Here, we critically review the mechanosensory lateral line (LL) system of zebrafish as a model to screen for chemical effects on neurosensory function of fish in particular and vertebrates in general. The LL system consists of so-called neuromasts, composed of centrally located sensory hair cells, and surrounding supporting cells. The function of neuromasts is the detection of water movements that is essential for the fish's ability to detect prey, to escape predator, to socially interact or to show rheotactic behaviour. Recent advances in the study of these organs provided researchers with a broad area of molecular tools for easy and rapid detection of neuromasts dysfunction and/or disturbed development. Further, genes involved in neuromasts differentiation have been identified using auditory/mechanosensory mutants and morphants. A number of environmental toxicants including metals and pharmaceuticals have been shown to affect neuromasts development and/or function. The use of the LL organ for toxicological studies offers the advantage to integrate the available profound knowledge on developmental biology of the neuromasts with the study of chemical toxicity. This combination may provide a powerful tool in environmental risk assessment.
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Close similarities of various physiological parameters makes the pig one of the preferred animal models for the study of human diseases, especially those involving the cardiovascular system. Unfortunately, the use of pig models to study diseases such as viral hemorrhagic fevers and endotoxic shock syndrome have been hampered by the lack of the necessary immunological tools to measure important immunoregulatory cytokines such as tumor necrosis factor (TNF). Here we describe a TNF-bioassay which is based on the porcine kidney cell line PK(15). Compared to the widely used murine fibroblastoid cell line L929, the PK(15) cell line displays a 100-1000-fold higher sensitivity for porcine TNF-alpha, a higher sensitivity for human TNF-alpha, and a slightly lower sensitivity for murine TNF-alpha. Using a PK(15) bioassay we can detect recombinant TNF-alpha as well as cytotoxic activity in the supernatants of lipopolysaccharide (LPS)-activated porcine monocytes at high dilutions. This suggests that the sensitivity of the test should permit the detection of TNF in biological specimens such as pig serum.
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PURPOSE To assess endometrial gene as well as protein expression of neuroendocrine and supposedly endometriosis-associated product PGP9.5 and pain symptoms in women with endometriosis and controls undergoing laparoscopy, using molecular biological and immuno-histochemical approaches in the same patients. METHODS Biopsy of eutopic endometrium from 29 patients by sharp curettage, and preparation of paraffin blocks. Determination of PGP9.5 gene expression and protein abundance using qPCR and immuno-histochemistry. RESULTS qPCR; The PGP9.5 mRNA expression level between women with (N = 16) and without (N = 13) endometriosis was not different, regardless of pain symptoms or menstrual cycle phase. PGP9.5 expression was higher in women who reported pain compared to those who did not; however, this association was not statistically significant. The expression of PGP9.5 mRNA was higher in women with endometriosis and pain during the proliferative than in the secretory phase (P = 0.03). Furthermore, in the first half of the cycle, the abundance of the PGP9.5 transcript was also significantly higher in endometriosis patients compared to those without (P = 0.03). Immuno-histochemistry; Thirteen of the 16 endometriosis patients showed positive PGP9.5 immuno-reactivity in the endometrium, whereas no such signal was observed in women without endometriosis. The absolute number of nerve fibres per mm(2) in women with endometriosis was similar, regardless of the pain symptoms. CONCLUSIONS PGP9.5 mRNA expression is increased in the proliferative phase of endometriotic women with pain. The presence of nerve fibres was demonstrated by a PGP9.5 protein signal in immuno-histochemistry and restricted to patients with endometriosis. Based on these results, however, there did not appear to be a direct association between the gene expression and protein abundance in women with and without endometriosis or those that experienced pain.
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Tropical forests are believed to be very harsh environments for human life. It is unclear whether human beings would have ever subsisted in those environments without external resources. It is therefore possible that humans have developed recent biological adaptations in response to specific selective pressures to cope with this challenge. To understand such biological adaptations we analyzed genome-wide SNP data under a Bayesian statistics framework, looking for outlier markers with an overly large extent of differentiation between populations living in a tropical forest, as compared to genetically related populations living outside the forest in Africa and the Americas. The most significant positive selection signals were found in genes related to lipid metabolism, the immune system, body development, and RNA Polymerase III transcription initiation. The results are discussed in the light of putative tropical forest selective pressures, namely food scarcity, high prevalence of pathogens, difficulty to move, and inefficient thermoregulation. Agreement between our results and previous studies on the pygmy phenotype, a putative prototype of forest adaptation, were found, suggesting that a few genetic regions previously described as associated with short stature may be evolving under similar positive selection in Africa and the Americas. In general, convergent evolution was less pervasive than local adaptation in one single continent, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures.
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The set of host- and pathogen-specific molecular features of a disease comprise its “signature”. We hypothesize that biological signatures enables distinctions between vaccinated vs. infected individuals. In our research, using porcine samples, protocols were developed that could also be used to identify biological signatures of human disease. Different classes of molecular features will be tested during this project, including indicators of basic immune capacity, which are being studied at this instance. These indicators of basic immune response such as porcine cytokines and antibodies were validated using Enzyme-linked immunosorbent assay (ELISA). This is an established method that detects antigens by their interaction with a specific antibody coupled to a polystyrene substrate. Serum from naïve and vaccinated pigs was tested for the presence of cytokines. We were able to differentiate the presence of porcine IL-6 in normal porcine serum with or without added porcine IL-6 by ELISA. In addition, four different cytokines were spotted on a grating-coupled surface plasmon resonance imaging system (GCSPRI) chip and antibody specific for IL-8 was run over the chip. Only the presence of IL-8 was detected; therefore, there was no cross-reactivity in this combination of antigens and antibodies. This system uses a multiplexed sensor chip to identify components of a sample run over it. The detection is accomplished by the change in refractive index caused by the interaction between the antibody spotted on the sensor chip and the antigen present in the sample. As the multiplexed GCSPRI is developed, we will need to optimize both sensitivity and specificity, minimizing the potential for cross-reactivity between individual analytes. The next step in this project is to increase the sensitivity of detection of the analytes. Currently, we are using two different antibodies (that recognize a different part of the antigen) to amplify the signal emitted by the interaction of antibody with its cognate antigen. The development of this sensor chip would not only allow to detect FMD virus, but also to differentiate between infected and vaccinated individuals, on location. Furthermore, the diagnosis of other diseases could be done with increased accuracy, and in less time due to the microarray approach.
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Documenting changes in distribution is necessary for understanding species' response to environmental changes, but data on species distributions are heterogeneous in accuracy and resolution. Combining different data sources and methodological approaches can fill gaps in knowledge about the dynamic processes driving changes in species-rich, but data-poor regions. We combined recent bird survey data from the Neotropical Biodiversity Mapping Initiative (NeoMaps) with historical distribution records to estimate potential changes in the distribution of eight species of Amazon parrots in Venezuela. Using environmental covariates and presence-only data from museum collections and the literature, we first used maximum likelihood to fit a species distribution model (SDM) estimating a historical maximum probability of occurrence for each species. We then used recent, NeoMaps survey data to build single-season occupancy models (OM) with the same environmental covariates, as well as with time- and effort-dependent detectability, resulting in estimates of the current probability of occurrence. We finally calculated the disagreement between predictions as a matrix of probability of change in the state of occurrence. Our results suggested negative changes for the only restricted, threatened species, Amazona barbadensis, which has been independently confirmed with field studies. Two of the three remaining widespread species that were detected, Amazona amazonica, Amazona ochrocephala, also had a high probability of negative changes in northern Venezuela, but results were not conclusive for Amazona farinosa. The four remaining species were undetected in recent field surveys; three of these were most probably absent from the survey locations (Amazona autumnalis, Amazona mercenaria and Amazona festiva), while a fourth (Amazona dufresniana) requires more intensive targeted sampling to estimate its current status. Our approach is unique in taking full advantage of available, but limited data, and in detecting a high probability of change even for rare and patchily-distributed species. However, it is presently limited to species meeting the strong assumptions required for maximum-likelihood estimation with presence-only data, including very high detectability and representative sampling of its historical distribution.
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Understanding changes over time in the distribution of interacting native and invasive species that may be symptomatic of competitive exclusion is critical to identify the need for and effectiveness of management interventions. Occupancy models greatly increase the robustness of inference that can be made from presence/absence data when species are imperfectly detected, and recent novel developments allow for the quantification of the strength of interaction between pairs of species. We used a two-species multi-season occupancy model to quantify the impact of the invasive American mink on the native European mink in Spain through the analysis of their co-occurrence pattern over twelve years (2000 - 2011) in the entire Spanish range of European mink distribution, where both species were detected by live trapping but American mink were culled. We detected a negative temporal trend in the rate of occupancy of European mink and a simultaneous positive trend in the occupancy of American mink. The species co-occurred less often than expected and the native mink was more likely to become extinct from sites occupied by the invasive species. Removal of American mink resulted in a high probability of local extinction where it co-occurred with the endemic mink, but the overall increase in the probability of occupancy over the last decade indicates that the ongoing management is failing to halt its spread. More intensive culling effort where both species co-exist as well as in adjacent areas where the invasive American mink is found at high densities is required in order to stop thedecline of European mink.
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Sensing systems in living bodies offer a large variety of possible different configurations and philosophies able to be emulated in artificial sensing systems. Motion detection is one of the areas where different animals adopt different solutions and, in most of the cases, these solutions reflect a very sophisticated form. One of them, the mammalian visual system, presents several advantages with respect to the artificial ones. The main objective of this paper is to present a system, based on this biological structure, able to detect motion, its sense and its characteristics. The configuration adopted responds to the internal structure of the mammalian retina, where just five types of cells arranged in five layers are able to differentiate a large number of characteristics of the image impinging onto it. Its main advantage is that the detection of these properties is based purely on its hardware. A simple unit, based in a previous optical logic cell employed in optical computing, is the basis for emulating the different behaviors of the biological neurons. No software is present and, in this way, no possible interference from outside affects to the final behavior. This type of structure is able to work, once the internal configuration is implemented, without any further attention. Different possibilities are present in the architecture to be presented: detection of motion, of its direction and intensity. Moreover, some other characteristics, as symmetry may be obtained.
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The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.
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Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.
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A highly sensitive assay combining immunomagnetic enrichment with multiparameter flow cytometric and immunocytochemical analysis has been developed to detect, enumerate, and characterize carcinoma cells in the blood. The assay can detect one epithelial cell or less in 1 ml of blood. Peripheral blood (10–20 ml) from 30 patients with carcinoma of the breast, from 3 patients with prostate cancer, and from 13 controls was examined by flow cytometry for the presence of circulating epithelial cells defined as nucleic acid+, CD45−, and cytokeratin+. Highly significant differences in the number of circulating epithelial cells were found between normal controls and patients with cancer including 17 with organ-confined disease. To determine whether the circulating epithelial cells in the cancer patients were neoplastic cells, cytospin preparations were made after immunomagnetic enrichment and were analyzed. Epithelial cells from patients with breast cancer generally stained with mAbs against cytokeratin and 3 of 5 for mucin-1. In contrast, no cells that stained for these antigens were observed in the blood from normal controls. The morphology of the stained cells was consistent with that of neoplastic cells. Of 8 patients with breast cancer followed for 1–10 months, there was a good correlation between changes in the level of tumor cells in the blood with both treatment with chemotherapy and clinical status. The present assay may be helpful in early detection, in monitoring disease, and in prognostication.