Lyme disease, caused by the bite of a deer tick infected with Borrelia burdorferi, has been increasing in distribution and prevalence annually throughout Maine. Worker’s compensation claims for Lyme disease have also been increasing steadily since the initial claim made in 1999. This research reviewed Maine worker’s compensation claims for Lyme disease from 1999-2011 to determine trends in state distribution and occupation type. Descriptive statistics were calculated to analyze different distributions of occupational Lyme disease. Occupations with the highest distribution of Lyme disease claims were those requiring workers to spend the majority of their time outdoors. A clear trend of claim distribution was seen, which mirrored that of the State of Maine Lyme disease case surveillance data. With the apparent increase in worker’s compensation claims due to Lyme disease and an increased geographic distribution annually, additional prevention and education efforts should be focused toward the higher risk occupations.

Borrelios är den vanligaste fästingburna infektionen på norra halvklotet, och orsakas av spiroketer tillhörande Borrelia burgdorferi sensu lato-komplexet. Dessa bakterier kan spridas till flera organ och ge upphov till olika symptom i bland annat hud, nervsystem, leder och hjärta. Omkring 15 % utvecklar neurologiska symptom, så kallad neuroborrelios. Den bästa indikatorn på aktiv neuroborrelios är framförallt karakteristiska neurologiska symptom samt tecken på en inflammatorisk förändring i cerebrospinalvätskan (CSV) i kombination med lokalt producerade antikroppar mot Borrelia burgdorferi s.l. i CSV. Nuvarande metod för diagnostik av neuroborrelios är en immunokemisk metod, en ELISA (enzyme-linked immunosorbent assay) som bygger på en jämförelse av Borrelia-antikroppsnivåer i CSV och i serum genom beräkning av antikroppsindex (AI). Beräkning av AI kompenserar för en eventuell ospecifik överföring av antikroppar från serum, till följd av en skada på blod-hjärnbarriären. Det finns dock tecken på att den nuvarande analysmetoden har för låg sensitivitet med falskt negativa resultat, framförallt tidigt i infektionsförloppet. För diagnostik av andra former av borrelios än neuroborrelios används en typ av ELISA baserad på C6-peptid. C6-peptid ELISA visar god känslighet för detektion av B. burgdorferi s.l.-specifika antikroppar i serum. C6-antigenet utgör en starkt immunogen och konserverad region av bakteriens VlsE-ytprotein. Syftet med den här studien var att undersöka om detektion av antikroppar mot C6-peptid i CSV kan komplettera den nuvarande använda metoden och därmed förbättra den totala sensitiviteten för diagnostik av neuroborrelios. I studien analyserades 169 patientprover från unga personer, samt 18 oklara patientfall som tidigare bedömts negativa med den nuvarande metoden. Antikroppar mot C6-peptid detekterades hos åtta unga patienter samt två oklara patientfall. Av dessa hade åtminstone tre unga patienter sannolikt neuroborrelios. Resultat från den här studien tyder på att C6-peptid-ELISA på CSV-prover kan fungera som ett komplement till befintlig metod för diagnostik av neuroborrelios. En kombination av båda metoderna kan sannolikt ge en betydligt högre sensitivitet. Vid tolkning av resultat från C6-peptid-baserade analysmetoder på CSV ska hänsyn tas till eventuell ospecifik överföring av B. burgdorferi s.l.-specifika antikroppar genom blod-hjärnbarriären. / Lyme Borreliosis, caused by spirochetes of the Borrelia burgdorferi sensu lato-complex, is the most common tick-borne infection in the temperate regions of the northern hemisphere. The bacteria can infect many different organs, this can give rise to a variety of symptoms in skin, the nervous system, joints and heart. Approximately 15 % of the infected individuals show neurological symptoms referred to as neuroborreliosis. An active neuroborreliosis is indicated by inflammatory changes in the cerebrospinal fluid (CSF) and local synthesis of anti-Borrelia antibodies in CSF. The current method to diagnose neuroborreliosis is an enzyme-linked immunosorbent assay (ELISA) which compares levels of anti-Borrelia antibodies in CSF and serum by calculating an antibody index (AI). Calculations of AI compensate for unspecific leakage of antibodies from serum to CSF following an injury of the blood-brain barrier. The drawback of the current method is a low sensitivity with a high rate of false negative results in samples collected early during an infection. Another type of ELISA, based on the use of a C6 peptide, has earlier shown good sensitivity for detection of B. burgdorferi s.l.-specific antibodies in serum. The C6 antigen corresponds to a highly immunogenic and conserved region of the bacterial surface protein VlsE. The aim of this study was to investigate whether a detection of antibodies against the C6 peptide in CSF could improve the total sensitivity for the diagnostics of neuroborreliosis. In the current study, 169 samples with negative AI from young patients and 18 samples from special cases were analyzed. Antibodies against the C6 peptide were found in 8 young patients and in 2 samples from special cases. Out of these, 3 young patients were stated positive for neuroborreliosis. Results of this study show that the C6 peptide ELISA on CSF samples could act as a complement to the current serological method for diagnosing neuroborreliosis. A combination of both methods could possibly increase the overall sensitivity. However, the blod-brain barrier injury issue is a problem in the analysis and interpretation of the results of the C6 peptide-based method on CSF should take into consideration a possible dysfunction of the blood-brain barrier. In conclusion, a combination of both the current method and the C6 peptide ELISA could give a markedly improved sensitivity in diagnostics of neuroborreliosis.

Diss. (sammanfattning) Umeå : Umeå universitet, 2010. / Härtill 5 uppsatser.

The proposed thesis contributes to the basic knowledge in tick (Ixodes ricinus) and tick-borne pathogens (Borrelia burgdorferi sensu lato, tick-borne encephalitis virus) ecology in particular studying the spatial distribution, host associations and its causes and consequences in Central European habitats.

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.

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.