La maladie de Lyme, ou borréliose de Lyme, est une infection bactérienne causée par le spirochète Borrelia burgdorferi sensu lato et transmise à l’hôte (homme, animal) par piqûre de tique du genre Ixodes. Cette maladie, caractérisée par un polymorphisme clinique important, est la maladie à transmission vectorielle la plus répandue dans l’hémisphère nord. Un traitement par antibiotiques permet une guérison rapide, mais si la maladie est diagnostiquée tardivement, certains symptômes persistent. Actuellement, aucun vaccin n’est commercialisé pour l’homme. Dans ce contexte, nous avons développé des approches protéomiques afin d’apporter de nouveaux éléments de compréhension du mécanisme d’interactions de la triade tique / bactérie / hôte. Ces travaux, visant particulièrement le développement de nouvelles stratégies vaccinales et diagnostiques, sont articulés autour de trois parties : - L’identification, suite à de nombreuses optimisations, d’une méthode d’analyse HPLC-UV et nanoLC-MS/MS, de protéines présentes dans des extraits de glandes salivaires de tiques et possédant une activité sur la réponse immunitaire innée cutanée. Ces développements ont mis en évidence une liste restreinte de protéines potentiellement bioactives. - La mise au point, sur un modèle murin, d’une méthode de détection d’une protéine de Borrelia burgdorferi, OspC, dans des biopsies cutanées par spectrométrie de masse ciblée LC-SRM. Cette étude a ouvert des perspectives quant au développement de nouveaux outils diagnostiques. - L’évaluation de la faisabilité de la détection de Borrelia burgdorferi directement à la surface de la peau par imagerie par spectrométrie de masse MALDI-MSI. / Lyme disease, or Lyme borreliosis, is a bacterial infection caused by Borrelia burgdorferi sensu lato and transmitted to the human or animal host by an Ixodes tick bite. This disease, characterized by a huge clinical polymorphism, is the most common vector-born disease in the Northern Hemisphere. An antibiotic treatment allows a fast recovery, but if it is diagnosed too late, some symptoms persist. Currently, no vaccine is marketed for humans. In this context, we have developed proteomic approaches to bring new understanding to the interaction mechanism of the triad tick / bacteria / host. This work, aimed particularly at the development of new vaccinal and diagnostic strategies, has three parts: - Identification, after numerous optimizations, of the analytical method HPLC-UV and nanoLC-MS/MS, of proteins that are present in tick salivary gland extracts and having activity on cutaneous innate immunity response. This work has highlighted a list of proteins with a potential biological activity. - Development, with a murine model, of a method for detecting Borrelia burgdorferi protein, OspC, in cutaneous biopsies by targeted mass spectrometry LC-SRM. This study has opened up perspectives concerning new diagnostic tools. - Evaluation of the feasibility of the Borrelia burgdorferi detection directly on the skin surface by MALDI imaging mass spectrometry.

The subject of the dissertation deals with the secondary school pupil's awareness of zoonoses. Zoonoses are the diseases transmited from animals to human beings. Those are one of the topics of biology study currently. The main aim of the dissertation is theoretical definition of the matter in the first place. For knowledge testing have been chosen these four illness: Toxoplasmosis, Lyme boreliossis, Rabies and Creuzfeldt-Jakobo disease. With these chosen illness the main research goal is to provide a comprehensive collection of information about the details such as disease transfer, spread of the disease, disease development, cure and avoidance. The research part of the dissertation tries to find out the extent of knowledge about the chosen kinds of zoonoses among the pupils at specialized secondary schools. As a tool of quantitative research has been used a pupil's didactic test. As based on the evaluated results we may say that the pupil's knowledge at explored schools is relatively satisfactory.

This thesis is devoted to designing and applying advanced algorithmical and statistical tools for analysis of NGS data related to cancer and infection diseases. NGS data under investigation are obtained either from host samples or viral variants. Recently, random peptide phage display libraries (RPPDL) were applied to studies of host's antibody response to different diseases. We study human antibody response to breast cancer and mouse antibody response to Lyme disease by sequencing of the whole antibody repertoire profiles which are represented by RPPDL. Alternatively, instead of sequencing immune response NGS can be applied directly to a viral population within an infected host. Specifically, we analyze the following RNA viruses: the human immunodeficiency virus (HIV) and the infectious bronchitis virus (IBV). Sequencing of RNA viruses is challenging because there are many variants inside population due to high mutation rate. Our results show that NGS helps to understand RNA viruses and explore their interaction with infected hosts. NGS also helps to analyze immune response to different diseases, trace changing of immune response at different disease stages.

The purpose of this study was to determine the prevalence of Lyme Disease (LD) bacteria in adult Ixodes pacificus ticks collected from the mountains of San Bernardino County in Southern California. Seven hundred fifty four I. pacificus adults were collected from the Pacific Crest Trail and adjacent areas. The Polymerase Chain Reaction (PCR) was used to screen ticks for Borrelia burgdorferi infection by targeting two different DNA loci. Oligonucleotide primers targeting both the ospA and fla genes were used in the assay. Ticks were processed in pools of three, and genomic DNA from the ticks was extracted with a commercial mini-kit utilizing silica matrix spin-columns. All ticks tested negative for B. burgdorferi infection regardless of primer pair used. In addition, ticks were negative following examination by dark-field microscopy. This study confirms previous reports that the prevalence of LD in Southern California is quite low.

La maladie de Lyme est une problématique d’actualité au Québec. Son émergence depuis la dernière décennie est vraisemblablement associée à des facteurs environnementaux et anthropiques qui favorisent la survie d’Ixodes scapularis et augmentent les risques d’exposition à ce vecteur de la maladie de Lyme. L’objectif de ce mémoire était d’estimer la vitesse et la direction de l’émergence de la maladie de Lyme au Québec et d’identifier les risques spatiotemporels. Une analyse de surface de tendance a été effectuée pour estimer la vitesse et la direction de son émergence en tenant compte du premier cas déclaré de maladie de Lyme pour chaque municipalité depuis 2004. Une analyse d’agrégats a aussi été effectuée pour identifier les régions à risque dans l’espace et le temps. Ces analyses ont été réalisées à la fois pour la date du début des symptômes et la date de déclaration de chaque cas de maladie de Lyme. Il est estimé que la maladie de Lyme se propage vers le nord du Québec à une vitesse variant entre 16 et 32 km/année selon la date de déclaration et la date de début des symptômes, respectivement. Un taux élevé de risque de maladie a été identifié dans sept agrégats au sud-ouest du Québec dans les régions sociosanitaires de la Montérégie et de l’Estrie. Les résultats obtenus dans cette étude amélioreront notre compréhension des patrons spatiotemporels de la maladie de Lyme au Québec, et pourront être utilisés dans des interventions proactives et ciblées par les autorités cliniques et de la santé publique. / Lyme disease is a current public health threat in Quebec. Its emergence over the last decade is likely caused by environmental and anthropological factors that favour the survival of Ixodes scapularis and increase the risk of exposure to this Lyme disease vector. The objective of this thesis was to estimate the speed and direction of Lyme disease emergence in Quebec and to identify spatiotemporal risk. A surface trend analysis was conducted to estimate the speed and direction of its emergence based upon the first detected case of Lyme disease in each municipality since 2004. A cluster analysis was also conducted to identify at-risk regions across space and time. These analyses were reproduced for the date of disease onset and date of notification for each case of Lyme disease. It was estimated that Lyme disease is spreading northward in Quebec at a speed varying between 16 and 32 km/year according to the date of notification and the date of disease onset, respectively. A high rate of disease risk was found in seven clusters identified in the south-west of Quebec in the sociosanitary regions of Montérégie and Estrie. The results obtained in this study improve our understanding of the spatiotemporal patterns of Lyme disease in Quebec, that can be used for proactive, targeted interventions by public and clinical health authorities.

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Over the past decade, and accelerated due to the COVID-19 pandemic, there has been increasing adoption of decentralized diagnostic testing, where the testing is brought closer to the patient. This trend has largely been fueled by the development of more accurate diagnostic tools and faster and more reliable data connectivity. Decentralized testing has been shown to greatly reduce turnaround times while increasing accessibility to users in remote regions. However, there are challenges that limit its widespread adoption. In this dissertation, we detail the development of tools and technologies to overcome these barriers and expedite the shift towards decentralized diagnostic testing. First, we demonstrate the ability to develop point-of-care (POC) diagnostic tests with performance that rivals that of traditional lab-based methods. We developed a rapid, multiplexed, microfluidic serological test for Lyme disease, a tick-borne disease caused by the Borrelia burgdorferi bacterium. The recommended testing, the standard 2-tiered (STT) approach, is not sensitive for early-stage infections, is labor-intensive, has long turnaround times, and requires the use of two immunoassays (enzyme-linked immunosorbent assay (ELISA) and the Western Blot). We developed a standalone multiplexed sandwich ELISA assay and adapted it to the mChip microfluidic platform. We validated the assay on a rigorously characterized panel of human serum samples and demonstrated that our approach outperforms the STT algorithm on sensitivity while matching its specificity. The form factor of this technology is amenable to use in physician’s offices and urgent care clinics. We also showed exploratory work towards adapting the mChip platform for diagnosis of Zika disease, a mosquito-borne disease caused by the Zika virus, and acute kidney injury, a syndrome characterized by loss of kidney excretory function. Next, we worked on increasing the adoption of rapid diagnostic tests for self- and partner-testing designed to be used in at-home settings. We developed a smartphone application to be used alongside the INSTI Multiplex test for detecting HIV and syphilis infections. The application was designed to provide users with i) instructions on running the test, ii) an automated deep-learning-based image interpretation algorithm to interpret the rapid test results from a smartphone image, iii) a way to save test results and display/share them, and iv) resources for follow-up care. We adopted a user-centered, iterative design process where we worked with a cohort of study participants composed of men who have sex with men and transgender women at high risk for contracting sexually transmitted infections. We then field tested the application with 48 participants over a duration of three months and found high acceptability for the application, both in terms of functionality and helpfulness. Finally, we sought to address a key limitation with deep-learning-based image classification techniques, specifically, the requirement for large numbers of annotated images for training. We developed a deep-learning image interpretation algorithm that could be quickly adapted to new rapid test kits using only a fraction of the images that would otherwise be needed for training the model. The interpretation algorithm followed a three-step, modular process. First, the rapid test kit and the membrane were extracted from the smartphone image. Second, the constituent zones were cropped from the extracted membrane. Finally, a classifier detected the presence or absence of a line in the individual zones. Fast adaptation was demonstrated by adapting a base model, trained using images of a single COVID-19 rapid test kit, to four different rapid test kits, each with different form factors, using few-shot domain adaptation. After training with 20 or fewer images, the classification accuracies of all the adapted models were > 95%. This approach can provide a digital health platform for improved pandemic preparedness and enable quality assurance and linkage to care for consumers operating new LFAs in widespread decentralized settings. Together, these methods provide a suite of tools that could expedite the shift towards decentralized, POC testing.

No description available.

This dissertation aims to develop methods for statistical predictions based on various types of data from different areas. We focus on applications from reliability and spatial epidemiology. Chapter 1 gives a general introduction of statistical predictions. Chapters 2 and 3 investigate the photodegradation of an organic coating, which is mainly caused by ultraviolet (UV) radiation but also affected by environmental factors, including temperature and humidity. In Chapter 2, we identify a physically motivated nonlinear mixed-effects model, including the effects of environmental variables, to describe the degradation path. Unit-to-unit variabilities are modeled as random effects. The maximum likelihood approach is used to estimate parameters based on the accelerated test data from laboratory. The developed model is then extended to allow for time-varying covariates and is used to predict outdoor degradation where the explanatory variables are time-varying. Chapter 3 introduces a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with random effects to describe the degradation paths and a vector time series model to describe the covariate process. Shape restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters of these models are estimated by using the maximum likelihood method. Algorithms for computing the estimated lifetime distribution are also described. The proposed methods are applied to predict the photodegradation path of an organic coating in a complicated dynamic environment. Chapter 4 investigates the Lyme disease emergency in Virginia at census tract level. Based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011, we analyze the spatial patterns of the disease using statistical smoothing techniques. We also use the space and space-time scan statistics to reveal the presence of clusters in the spatial and spatial/temporal distribution of Lyme disease. Chapter 5 builds a predictive model for Lyme disease based on historical data and environmental/demographical information of each census tract. We propose a Divide-Recombine method to take advantage of parallel computing. We compare prediction results through simulation studies, which show our method can provide comparable fitting and predicting accuracy but can achieve much more computational efficiency. We also apply the proposed method to analyze Virginia Lyme disease spatio-temporal data. Our method makes large-scale spatio-temporal predictions possible. Chapter 6 gives a general review on the contributions of this dissertation, and discusses directions for future research. / Ph. D.

Over the last 20 years, Lyme disease has grown to become the most common vector-borne disease affecting Americans. Spread in the eastern U.S. primarily by the bite of Ixodes scapularis, the black-legged tick, the disease affects an estimated 329,000 Americans per year. Originally confined to New England, it has since spread across much of the east coast and has become endemic in Virginia. Since 2010 the state has averaged 1200 cases per year, with 200 annually in the New River Health District (NRHD), the location of our study. Efforts to geographically model Lyme disease primarily focus on landscape and climatic variables. The disease depends highly on the survival of the tick vector, and white-footed mouse, the primary reservoir. Both depend on the existence of forest-herbaceous edge-habitats, as well as warm summer temperatures, mild winter lows, and summer wetness. While many studies have investigated the effect of forest fragmentation on Lyme, none have made use of high-resolution land cover data to do so at the peridomestic level. To fill this knowledge gap, we made use of the Virginia Geographic Information Network’s 1-meter land cover dataset and identified forest-herbaceous edge-habitats for the NRHD. We then calculated the density of these edge-habitats at 100, 200 and 300-meter radii, representing the peridomestic environment. We also calculated the density of <2-hectare forest patches at the same distance thresholds. To avoid confounding from climatic variation, we also calculated mean summer temperatures, total summer rainfall, and number of consecutive days below freezing of the prior winters. Adding to these data, elevation, terrain shape index, slope, and aspect, and including lags on each of our climatic variables, we created environmental niche models of Lyme in the NRHD. We did so using both Boosted Regression Trees (BRT) and Maximum Entropy (MaxEnt) modeling, the two most common niche modeling algorithms in the field today. We found that Lyme is strongly associated with higher density of developed-herbaceous edges within 100-meters from the home. Forest patch density was also significant at both 100-meter and 300-meter levels. This supports the notion that the fine scale peridomestic environment is significant to Lyme outcomes, and must be considered even if one were to account for fragmentation at a wider scale, as well as variations in climate and terrain. / M.S. / Lyme disease is the most common vector-borne disease in the United States today. Infecting about 330,000 Americans per year, the disease continues to spread geographically. Originally found only in New England, the disease is now common in Virginia. The New River Health District, where we did our study, sees over 200 cases per year. Lyme disease is mostly spread by the bite of the black-legged tick. As such we can predict where Lyme cases might be found if we understand the environmental needs of these ticks. The ticks themselves depend on warm summer temperatures, mild winter lows, and summer wetness. But they are also affected by forest fragmentation which drives up the population of white-footed mice, the tick’s primary host. The mice are particularly fond of the interface between forests and open fields. These edge habitats provide food and cover for the mice, and in turn support a large population of ticks. Many existing studies have demonstrated this link, but all have done so across broad scales such as counties or census tracts. To our knowledge, no such studies have investigated forest fragmentation near the home of known Lyme cases. To fill this gap in our knowledge, we made use of high-resolution forest cover data to identify forest-field edge habitats and small isolated forest patches. We then calculated the total density of both within 100, 200 and 300 meters of the homes of known Lyme cases, and compared these to values from non-cases using statistical modeling. We also included winter and summer temperatures, rainfall, elevation, slope, aspect, and terrain shape. We found that a large amount of forest-field edges within 100 meters of a home increases the risk of Lyme disease to residents of that home. The same can be said for isolated forest patches. Even after accounting for all other variables, this effect was still significant. This information can be used by health departments to predict which neighborhoods may be most at risk for Lyme. They can then increase surveillance in those areas, warn local doctors, or send out educational materials.