11 research outputs found
Advances in Forensic Genetics
The book has 25 articles about the status and new directions in forensic genetics. Approximately half of the articles are invited reviews, and the remaining articles deal with new forensic genetic methods. The articles cover aspects such as sampling DNA evidence at the scene of a crime; DNA transfer when handling evidence material and how to avoid DNA contamination of items, laboratory, etc.; identification of body fluids and tissues with RNA; forensic microbiome analysis with molecular biology methods as a supplement to the examination of human DNA; forensic DNA phenotyping for predicting visible traits such as eye, hair, and skin colour; new ancestry informative DNA markers for estimating ethnic origin; new genetic genealogy methods for identifying distant relatives that cannot be identified with conventional forensic DNA typing; sensitive DNA methods, including single-cell DNA analysis and other highly specialised and sensitive methods to examine ancient DNA from unidentified victims of war; forensic animal genetics; genetics of visible traits in dogs; statistical tools for interpreting forensic DNA analyses, including the most used IT tools for forensic STR-typing and DNA sequencing; haploid markers (Y-chromosome and mitochondria DNA); inference of ethnic origin; a comprehensive logical framework for the interpretation of forensic genetic DNA data; and an overview of the ethical aspects of modern forensic genetics
Genetics and Improvement of Forest Trees
Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests. However, given the drastically changing global environment, improvement of various traits related to environmental adaptability is more essential than ever. This book focuses on genetic information, including trait heritability and the physiological mechanisms thereof, which facilitate tree improvement. Nineteen papers are included, reporting genetic approaches to improving various species, including conifers, broad-leaf trees, and bamboo. All of the papers in this book provide cutting-edge genetic information on tree genetics and suggest research directions for future tree improvement
Forensic Analysis
It is my pleasure to place before you the book ''Forensic Analysis - From Death to Justice'' which presents one of the major portions of the broad specialty of Forensic Science comprising mainly of Thanatology and Criminalistics. This book has been designed to incorporate a wide range of new ideas and unique works from all authors from topics like Forensic Engineering, Forensic Entomology and Crime Scene Investigation. I hope that it will be useful to practitioners of forensic medicine, experts, pathologists, law makers, investigating authorities, undergraduate and postgraduate medical school graduates of medicine
Human neutrophil elastase phenotyping: classifying neutrophils by function with novel imaging agents
This thesis describes approaches taken to classify the functionally diverse neutrophil
by its different functions in a live-cell imaging tool. Human neutrophil elastase
(HNE) cleaves over 30 substrates, its release is controlled with varying extents of
degranulation and its activity is subject to complex modulation. HNE regulation is
necessary for the maintenance of health via HNE’s multiplicity of functions.
Although neutrophils are routinely quantified in assessments of chronic
inflammation, we do not have a clinically translatable, integrated tool for measuring
HNE in human tissues. Two novel probes have been characterised and multiplexed
to form the basis of an imaging technique for understanding the functional
implications of neutrophil activity in human tissues, in real-time. Neutrophil
Activation Probe (NAP) and VE200 are novel probes for HNE activity and presence.
These probes are characterised in vitro as sensitive and HNE-specific imaging agents
for live-cell imaging and image cytometry.
Multiplexed NAP and HNE are detect neutrophil activation, apoptosis and necrosis.
These imaging agents can inform deep profiling techniques to separate neutrophils
into untreated, primed, activated and primed-activated states and endogenous vs.
exogenous stimulus sub-states. Finally, sections of adenocarcinomatous human lung
and whole human lungs, ventilated ex vivo, demonstrate the applicability of HNEbased,
multiparametric profiling and neutrophil activation detection to clinically
relevant platforms
Études multiparamétriques de biomarqueurs par immunofluorescence pour mieux suivre la progression du cancer de la prostate
Le cancer de la prostate est le cancer le plus fréquemment diagnostiqué et la troisième cause de mortalité liée au cancer chez les hommes au Canada. Un quart des patients diagnostiqués développeront une forme plus agressive de ce cancer. Bien que nous possédions plusieurs indices cliniques pronostiques dans les cancers localisés (score de Gleason, taux sérique d’antigène prostatique spécifique (APS), stade, etc.), ceux-ci sont insuffisants pour adéquatement distinguer les patients à faible risque de progression de ceux à haut risque. A ce jour, aucun biomarqueur pronostique n’est encore utilisé en clinique. Les cliniciens ont donc besoin de nouveaux outils plus efficaces pour stratifier ce cancer et pour s’assurer d’adapter au mieux le traitement à chaque patient.
En nous basant sur la littérature et sur des études préliminaires (cohortes de moins de 65 patients), notre hypothèse est que les protéines PUMA-NOXA et les récepteurs membranaires de la famille ERBB seraient, lorsqu’utilisés en combinaison, des biomarqueurs prédictifs de la progression du cancer de la prostate. Les objectifs de cette thèse sont : 1) identifier et valider de bons anticorps pour chaque biomarqueur d’intérêt, 2) définir les niveaux d’expression de chaque biomarqueur sur une cohorte de 285 patients, et 3) établir les corrélations entre les niveaux d’expression et les données cliniques des patients.
Dans l’optique d’une utilisation en clinique, des anticorps de type monoclonal ont été choisis pour identifier les biomarqueurs d’intérêts. Ces anticorps ont été testés et validés pour leur spécificité par immunobuvardage de type western blot et par immunofluorescence. La localisation de la protéine d’intérêt a été validée sur des échantillons de tissus de patients suivie de l’optimisation du multi-marquage sur les cellules épithéliales et basales. Après perfectionnement de l’analyse d’images, nous avons montré qu’une expression extrême (faible ou forte) de PUMA couplée à une forte expression de NOXA dans les glandes bénignes est associée à la rechute biochimique des patients. La présence de ces biomarqueurs dans les glandes bénignes permet d’envisager d’améliorer l’identification lors des premières biopsies des patients se qualifiant pour la surveillance active. Par ailleurs, le suivi de l’expression des récepteurs de la famille ERBB dans les glandes tumorales permet une stratification des patients atteints d’un cancer de la prostate en fonction des risques de rechute biochimique, de développement de métastases et de mort liée au cancer. Ainsi, les patients présentant la combinaison d’une forte expression de EGFR et d’une faible expression de ERBB3 sont les plus susceptibles de mourir spécifiquement de leur cancer de la prostate, en particulier si les cellules tumorales présentes en plus une faible expression de ERBB2 entrainant un fort risque de développer des métastases.
Mon projet de doctorat aura donc permis d’identifier et de valider des biomarqueurs d’intérêt pour prédire l’évolution du cancer de la prostate et démontrer l’intérêt de suivre ces biomarqueurs en combinaison afin d’obtenir une meilleure stratification des patients. Ces résultats devront être validés sur une cohorte indépendante et multicentrique en vue de fournir aux cliniciens un plus grand nombre d’outils pour leur permettre de réaliser une stratification fine des patients atteints d’un cancer de la prostate, et ouvrirait la voie à de nouvelles stratégies thérapeutiques plus ciblées.Prostate cancer is the most frequently diagnosed cancer and the third leading cause of cancer-related death in men in Canada. A quarter of patients will develop a more aggressive form of this cancer. While there are several clinical prognostic variables for localized prostate cancer (Gleason score, prostate specific antigen (PSA) levels, stage, etc.), these are insufficient to adequately distinguish between low and high-risk of progression cases. As a result, clinicians need new, more effective tools to stratify this cancer and to ensure that treatments are best tailored to each patient. To date, no prognostic biomarker has yet been used clinically.
Based on the literature and preliminary studies of small cohorts (less than 65 patients), we hypothesize that the protein expression of PUMA-NOXA and ERBB family members could help with the prediction of prostate cancer progression. The objectives of this thesis are: 1) to identify and validate antibodies for each biomarker of interest, 2) to define the expression levels of each biomarker on a 285 patient cohort, 3) to evaluate the correlation between marker expression levels and patient clinical data.
For clinical use, monoclonal-type antibodies were chosen to identify the biomarkers of interest. These antibodies were validated for specificity by western blot and immunofluorescence techniques. The localization of the protein of interest was further identified within samples of patient tissues and additional optimization involving combinatorial staining for epithelial and basal cells. After refining the imaging and statistical analysis of PUMA and NOXA in benign glands, we found that extreme (weak or strong) PUMA expression coupled with high NOXA expression was associated with biochemical relapse. In addition, these proteins have significant potential for predicting disease evolution based on the initial radical prostatectomy sample. The presence of these proteins in benign glands would allow the identification of patients less suitable for active surveillance. Additionally, statistical analysis of ERBB family receptors in tumor glands, when used alone, allow stratification of prostate cancer patients for the prediction of biochemical relapse, development of metastases and also specific death from prostate cancer. Moreover, patients expressing a combination of high EGFR expression and low ERBB3 expression are at high risk of biochemical relapse and are at higher risk of prostate cancer specific mortality. In addition, coupling this high EGFR – low ERBB3 combination to a low ERBB2 expression helps classify patients at high risk of developing metastases.
My doctoral research project will have made it possible to identify and validate biomarkers of interest for predicting the progression of prostate cancer and demonstrating the interest of combining these biomarkers in order to achieve better stratification of patients with prostate cancer. In the context of clinical utility, these results need to be validated on an independent and multicenter cohort in order to confirm these findings. This would eventually provide clinicians with a greater number of tools at their disposal to correctly anticipate patient trajectories and possibly identify new therapeutic targets for the control of the disease
ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests
Image segmentation is a necessary step in automated quantitative imaging. ImageSURF is a macro-compatibleImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives totrain pixel classifiers which are then applied to image sets of any size to produce segmentations withoutbias in a consistent, transparent and reproducible manner. The plugin is available from ImageJ update sitehttp://sites.imagej.net/ImageSURF/ and source code from https://github.com/omaraa/ImageSURF
Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers
The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Histopathological image analysis: a review,”
Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe