978 resultados para diagnostic fluorescent PCR


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Zakład Biofizyki Molekularnej, Centrum NanoBioMedyczne UAM

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Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

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Trophoblasts of the placenta are the frontline cells involved in communication and exchange of materials between the mother and fetus. Within trophoblasts, calcium signalling proteins are richly expressed. Intracellular free calcium ions are a key second messenger, regulating various cellular activities. Transcellular Ca2+ transport through trophoblasts is essential in fetal skeleton formation. Ryanodine receptors (RyRs) are high conductance cation channels that mediate Ca2+ release from intracellular stores to the cytoplasm. To date, the roles of RyRs in trophoblasts have not been reported. By use of reverse transcription PCR and western blotting, the current study revealed that RyRs are expressed in model trophoblast cell lines (BeWo and JEG-3) and in human first trimester and term placental villi. Immunohistochemistry of human placental sections indicated that both syncytiotrophoblast and cytotrophoblast cell layers were positively stained by antibodies recognising RyRs; likewise, expression of RyR isoforms was also revealed in BeWo and JEG-3 cells by immunofluorescence microscopy. In addition, changes in [Ca2+]i were observed in both BeWo and JEG-3 cells upon application of various RyR agonists and antagonists, using fura-2 fluorescent videomicroscopy. Furthermore, endogenous placental peptide hormones, namely angiotensin II, arginine vasopressin and endothelin 1, were demonstrated to increase [Ca2+]i in BeWo cells, and such increases were suppressed by RyR antagonists and by blockers of the corresponding peptide hormone receptors. These findings indicate that 1) multiple RyR subtypes are expressed in human trophoblasts; 2) functional RyRs in BeWo and JEG-3 cells response to both RyR agonists and antagonists; 3) RyRs in BeWo cells mediate Ca2+ release from intracellular store in response to the indirect stimulation by endogenous peptides. These observations suggest that RyR contributes to trophoblastic cellular Ca2+ homeostasis; trophoblastic RyRs are also involved in the functional regulation of human placenta by coupling to endogenous placental peptide-induced signalling pathways.

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The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

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The overall objective of this thesis is to integrate a number of micro/nanotechnologies into integrated cartridge type systems to implement such biochemical protocols. Instrumentation and systems were developed to interface such cartridge systems: (i) implementing microfluidic handling, (ii) executing thermal control during biochemical protocols and (iii) detection of biomolecules associated with inherited or infectious disease. This system implements biochemical protocols for DNA extraction, amplification and detection. A digital microfluidic chip (ElectroWetting on Dielectric) manipulated droplets of sample and reagent implementing sample preparation protocols. The cartridge system also integrated a planar magnetic microcoil device to generate local magnetic field gradients, manipulating magnetic beads. For hybridisation detection a fluorescence microarray, screening for mutations associated with CFTR gene is printed on a waveguide surface and integrated within the cartridge. A second cartridge system was developed to implement amplification and detection screening for DNA associated with disease-causing pathogens e.g. Escherichia coli. This system incorporates (i) elastomeric pinch valves isolating liquids during biochemical protocols and (ii) a silver nanoparticle microarray for fluorescent signal enhancement, using localized surface plasmon resonance. The microfluidic structures facilitated the sample and reagent to be loaded and moved between chambers with external heaters implementing thermal steps for nucleic acid amplification and detection. In a technique allowing probe DNA to be immobilised within a microfluidic system using (3D) hydrogel structures a prepolymer solution containing probe DNA was formulated and introduced into the microfluidic channel. Photo-polymerisation was undertaken forming 3D hydrogel structures attached to the microfluidic channel surface. The prepolymer material, poly-ethyleneglycol (PEG), was used to form hydrogel structures containing probe DNA. This hydrogel formulation process was fast compared to conventional biomolecule immobilization techniques and was also biocompatible with the immobilised biomolecules, as verified by on-chip hybridisation assays. This process allowed control over hydrogel height growth at the micron scale.

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BACKGROUND: Tissue transglutaminase (t-TG) is the main autoantigen recognized by the endomysium antibodies (EMA) observed in patients with celiac disease (CD). The aim of the study was to assess an ELISA method for t-TG antibodies (t-TGA) with respect to EMA IF assay in pediatric and adult patients. METHODS: t-TGA were analyzed by ELISA in 220 sera samples: 82 patients with biopsy-proven untreated CD (23 adults and 59 children), 14 CD children on gluten-free diet, 18 asymptomatic relatives of CD patients, and 106 age-matched control patients with gluten-unrelated gastrointestinal diseases (58 adults and 48 children). Serum IgA EMA were tested on umbilical cord sections in all patients. RESULTS: The great majority (92.7%) of untreated CD patients (both adults and children) were t-TGA positive (values ranging from 20.1 to > 300 AU). None of the child control patients and only two out of 58 (3.4%) of the adults with unrelated gastrointestinal diseases had serum t-TGA positivity; two out of 18 first-degree relatives with biopsy-proved silent CD were t-TGA (as well as EMA) positive. Finally, two out of 14 CD children, assuming a gluten-free diet, had serum t-TGA (as well as EMA). A highly significant correlation (P < 0.001) was observed between t-TGA concentrations and EMA. t-TGA showed a sensitivity of 87% and 95%, a specificity of 97% and 100% for adults and children, respectively. CONCLUSION: The method is highly sensitive and specific in the diagnosis of CD and is promising as a tool for routine diagnostic use and population screening, especially in children.

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BACKGROUND: Serologic methods have been used widely to test for celiac disease and have gained importance in diagnostic definition and in new epidemiologic findings. However, there is no standardization, and there are no reference protocols and materials. METHODS: The European working group on Serological Screening for Celiac Disease has defined robust noncommercial test protocols for immunoglobulin (Ig)G and IgA gliadin antibodies and for IgA autoantibodies against endomysium and tissue transglutaminase. Standard curves were linear in the decisive range, and intra-assay variation coefficients were less than 5% to 10%. Calibration was performed with a group reference serum. Joint cutoff limits were used. Seven laboratories took part in the final collaborative study on 252 randomized sera classified by histology (103 pediatric and adult patients with active celiac disease, 89 disease control subjects, and 60 blood donors). RESULTS: IgA autoantibodies against endomysium and tissue transglutaminase rendered superior sensitivity (90% and 93%, respectively) and specificity (99% and 95%, respectively) over IgA and IgG gliadin antibodies. Tissue transglutaminase antibody testing showed superior receiver operating characteristic performance compared with gliadin antibodies. The K values for interlaboratory reproducibility showed superiority for IgA endomysium (0.93) in comparison with tissue transglutaminase antibodies (0.83) and gliadin antibodies (0.82 for IgG, 0.62 for IgA). CONCLUSIONS: Basic criteria of standardization and quality assessment must be fulfilled by any given test protocol proposed for serologic investigation of celiac disease. The working group has produced robust test protocols and reference materials available for standardization to further improve reliability of serologic testing for celiac disease.

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Efficient early identification of primary immunodeficiency disease (PID) is important for prognosis, but is not an easy task for non-immunologists. The Clinical Working Party of the European Society for Immunodeficiencies (ESID) has composed a multi-stage diagnostic protocol that is based on expert opinion, in order to increase the awareness of PID among doctors working in different fields. The protocol starts from the clinical presentation of the patient; immunological skills are not needed for its use. The multi-stage design allows cost-effective screening for PID within the large pool of potential cases in all hospitals in the early phases, while more expensive tests are reserved for definitive classification in collaboration with an immunologist at a later stage. Although many PIDs present in childhood, others may present at any age. The protocols presented here are therefore aimed at both adult physicians and paediatricians. While designed for use throughout Europe, there will be national differences which may make modification of this generic algorithm necessary.

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In countries where the incidence of tuberculosis is low, perinatal tuberculosis is seldom diagnosed. With increasing numbers of human immunodeficiency virus-infected people and increasing immigrant population from high tuberculosis incidence countries, one might expect perinatal tuberculosis to become more frequent. Early recognition of newborns at risk for perinatal tuberculosis infection is of utmost importance to prevent disease by chemoprophylaxis. We describe a case of latent perinatal tuberculosis infection in a newborn infected from a mother with extrapulmonary primary tuberculosis. Tuberculin skin test was negative, and latent tuberculosis infection was eventually diagnosed by specific immunological tests. We discuss the difficulties in diagnosis of recent tuberculosis infection in neonates and infants, and the risk factors for vertical transmission of tuberculosis, which need to be taken into account in considering the need for chemoprophylaxis in the newborn. Although perinatal TB infection is a rare condition and diagnosis is difficult due to poor diagnostic testing in pregnancy and newborns, a high index of suspicion is needed to limit the diagnostic delay and to avoid progression to perinatal TB disease.

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Severe primary immunodeficiencies (PID) are rare; their global incidence is comparable to that of childhood leukemia; they include more than 100 different entities. Clinical manifestations are: unusually severe or frequent infections or infections that do not respond to adequate treatment; an increased risk of certain malignancies; sometimes auto-immune manifestations. Delayed diagnosis and management of PID can lead to severe and irreversible complications or to death. PID can become manifest only in the adult; in common variable immune deficiency, the median age at diagnosis is between the 2nd and the 3rd decade of life. PID are often transmitted genetically; recent progresses in molecular biology have allowed more precise and earlier, including antenatal, diagnosis. Molecular treatment of 3 infants with a severe immunodeficiency has recently been achieved in April 2000. Those progresses were mostly based on the study of immunodeficiency databases. We present here the work of a Belgian group specialized in PID; meetings have started in June 1997. This group establishes guidelines for the diagnosis and treatment of PID, adapted to the local situation. The elaboration of a national register of PID is also underway; this has to provide all guaranties of anonymity to patients and families. Such a register already exists at the European level; it has provided the basis for new diagnostic and therapeutic possibilities. The inclusion of Belgian data in this register should allow essential progresses essential for our patients.

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The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

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We apply a coded aperture snapshot spectral imager (CASSI) to fluorescence microscopy. CASSI records a two-dimensional (2D) spectrally filtered projection of a three-dimensional (3D) spectral data cube. We minimize a convex quadratic function with total variation (TV) constraints for data cube estimation from the 2D snapshot. We adapt the TV minimization algorithm for direct fluorescent bead identification from CASSI measurements by combining a priori knowledge of the spectra associated with each bead type. Our proposed method creates a 2D bead identity image. Simulated fluorescence CASSI measurements are used to evaluate the behavior of the algorithm. We also record real CASSI measurements of a ten bead type fluorescence scene and create a 2D bead identity map. A baseline image from filtered-array imaging system verifies CASSI's 2D bead identity map.

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BACKGROUND: One year after the introduction of Information and Communication Technology (ICT) to support diagnostic imaging at our hospital, clinicians had faster and better access to radiology reports and images; direct access to Computed Tomography (CT) reports in the Electronic Medical Record (EMR) was particularly popular. The objective of this study was to determine whether improvements in radiology reporting and clinical access to diagnostic imaging information one year after the ICT introduction were associated with a reduction in the length of patients' hospital stays (LOS). METHODS: Data describing hospital stays and diagnostic imaging were collected retrospectively from the EMR during periods of equal duration before and one year after the introduction of ICT. The post-ICT period was chosen because of the documented improvement in clinical access to radiology results during that period. The data set was randomly split into an exploratory part used to establish the hypotheses, and a confirmatory part. The data was used to compare the pre-ICT and post-ICT status, but also to compare differences between groups. RESULTS: There was no general reduction in LOS one year after ICT introduction. However, there was a 25% reduction for one group - patients with CT scans. This group was heterogeneous, covering 445 different primary discharge diagnoses. Analyses of subgroups were performed to reduce the impact of this divergence. CONCLUSION: Our results did not indicate that improved access to radiology results reduced the patients' LOS. There was, however, a significant reduction in LOS for patients undergoing CT scans. Given the clinicians' interest in CT reports and the results of the subgroup analyses, it is likely that improved access to CT reports contributed to this reduction.

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Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.