Thèses : « Early Detection of Parkinson's Disease »

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Figueiredo, Isabel De. « Early Detection of Parkinson's Disease through Microfluidics and Ion Mobility - Mass Spectrometry Integration ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASF070.

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L'alpha-synucléine est un biomarqueur crucial pour la maladie de Parkinson, mais sa détection précoce est difficile en raison de sa faible abondance et de la nature intrinsèquement désordonnée de la protéine. Le développement de méthodes de diagnostic précoces repose fortement sur la compréhension et la différenciation des caractéristiques structurales de l'alpha-synucléine native par rapport à ses formes pathologiques, ces variations offrant des informations précieuses sur le début et la progression de la maladie. Cette thèse de doctorat examine le paysage conformationnel de l'alpha-synucléine et explore les techniques permettant de capturer et de concentrer cette protéine sans altérer sa structure. Deux types de dispositifs microfluidiques sont présentés : le premier intègre un module de micro-immunopurification optimisé pour la capture de l'alpha-synucléine et un module de micro-chromatographie d'exclusion de taille conçu pour le déssalement et l'échange de tampon pour être détecté par Spectrométrie de Masse couplée avec la Mobilité Ionique. De plus, une puce intégrée 2-en-1 combine ces deux modules en une seule plateforme, simplifiant le processus expérimental pour une efficacité et une précision accrues dans l'analyse de l'alpha-synucléine. Le couplage de ces dispositifs microfluidiques avec la Spectrométrie de Masse et la Mobilité Ionique permet la caractérisation structurale de l'alpha-synucléine, contribuant ainsi au développement de méthodes de diagnostic précoces en permettant la différenciation des abondances des conformères entre les formes natives et pathologiques de la protéine
Alpha-synuclein is a critical biomarker for Parkinson's disease, however its early detection is challenging due to its low abundance and intrinsically disordered protein nature. The development of early diagnostic methods relies heavily on understanding and differentiating the structural characteristics of native alpha-synuclein versus its pathological forms, as these variations provide valuable insights into disease onset and progression. This Ph.D. thesis, investigates the conformational landscape of alpha-synuclein and explores techniques to capture and concentrate this protein without disrupting its structure. Two types of microfluidic devices are presented: the first device integrates a micro-immunopurification module optimized for alpha-synuclein capture and a micro-size exclusion chromatography module designed for desalting and buffer exchange to facilitate coupling with Ion Mobility-Mass Spectrometry. Additionally, an integrated 2-in-1 chip combines these modules into a single platform, streamlining the workflow for enhanced efficiency and accuracy in alpha-synuclein analysis. The coupling of these microfluidic devices with the Ion Mobility-Mass Spectrometry advances the structural characterization of alpha-synuclein, contributing to the development of early diagnostic methods by enabling the differentiation between native and pathological forms of the protein

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Filali, razzouki Anas. « Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAS002.

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Cette thèse vise à développer des biomarqueurs numériques robustes pour la détection précoce de la maladie de Parkinson (MP) en analysant des vidéos faciales afin d'identifier les changements associés à l'hypomimie. Dans ce contexte, nous introduisons de nouvelles contributions à l'état de l'art : l'une fondée sur l'apprentissage automatique superficiel et l'autre fondée sur l'apprentissage profond. La première méthode utilise des modèles d'apprentissage automatique qui exploitent des caractéristiques faciales extraites manuellement, en particulier les dérivés des unités d'action faciale (AUs). Ces modèles intègrent des mécanismes d'interprétabilité qui permettent d'expliquer leur processus de décision auprès des parties prenantes, mettant en évidence les caractéristiques faciales les plus distinctives pour la MP. Nous examinons l'influence du sexe biologique sur ces biomarqueurs numériques, les comparons aux données de neuroimagerie et aux scores cliniques, et les utilisons pour prédire la gravité de la MP. La deuxième méthode exploite l'apprentissage profond pour extraire automatiquement des caractéristiques à partir de vidéos faciales brutes et des données de flux optique en utilisant des modèles fondamentaux basés sur les Vision Transformers pour vidéos. Pour pallier le manque de données d'entraînement, nous proposons des techniques avancées d'apprentissage par transfert adaptatif, en utilisant des modèles fondamentaux entraînés sur de grands ensembles de données pour la classification de vidéos. De plus, nous intégrons des mécanismes d'interprétabilité pour établir la relation entre les caractéristiques extraites automatiquement et les AUs faciales extraites manuellement, améliorant ainsi la clarté des décisions des modèles. Enfin, nos caractéristiques faciales générées proviennent à la fois de données transversales et longitudinales, ce qui offre un avantage significatif par rapport aux travaux existants. Nous utilisons ces enregistrements pour analyser la progression de l'hypomimie au fil du temps avec ces marqueurs numériques, et sa corrélation avec la progression des scores cliniques. La combinaison des deux approches proposées permet d'obtenir une AUC (Area Under the Curve) de classification de plus de 90%, démontrant l'efficacité des modèles d'apprentissage automatique et d'apprentissage profond dans la détection de l'hypomimie chez les patients atteints de MP à un stade précoce via des vidéos faciales. Cette recherche pourrait permettre une surveillance continue de l'hypomimie en dehors des environnements hospitaliers via la télémédecine
This thesis aims to develop robust digital biomarkers for early detection of Parkinson's disease (PD) by analyzing facial videos to identify changes associated with hypomimia. In this context, we introduce new contributions to the state of the art: one based on shallow machine learning and the other on deep learning.The first method employs machine learning models that use manually extracted facial features, particularly derivatives of facial action units (AUs). These models incorporate interpretability mechanisms that explain their decision-making process for stakeholders, highlighting the most distinctive facial features for PD. We examine the influence of biological sex on these digital biomarkers, compare them against neuroimaging data and clinical scores, and use them to predict PD severity.The second method leverages deep learning to automatically extract features from raw facial videos and optical flow using foundational models based on Video Vision Transformers. To address the limited training data, we propose advanced adaptive transfer learning techniques, utilizing foundational models trained on large-scale video classification datasets. Additionally, we integrate interpretability mechanisms to clarify the relationship between automatically extracted features and manually extracted facial AUs, enhancing the comprehensibility of the model's decisions.Finally, our generated facial features are derived from both cross-sectional and longitudinal data, which provides a significant advantage over existing work. We use these recordings to analyze the progression of hypomimia over time with these digital markers, and its correlation with the progression of clinical scores.Combining these two approaches allows for a classification AUC (Area Under the Curve) of over 90%, demonstrating the efficacy of machine learning and deep learning models in detecting hypomimia in early-stage PD patients through facial videos. This research could enable continuous monitoring of hypomimia outside hospital settings via telemedicine

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Taleb, Catherine. « Parkinson's desease detection by multimodal analysis combining handwriting and speech signals ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT039.

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La maladie de Parkinson (MP) est un trouble neurologique causé par une diminution du niveau de dopamine dans le cerveau. Cette maladie est caractérisée par des symptômes moteurs et non moteurs qui s'aggravent avec le temps. Aux stades avancés de la maladie de Parkinson, le diagnostic clinique est clair. Cependant, dans les premiers stades, lorsque les symptômes sont souvent incomplets ou subtils, le diagnostic devient difficile et, parfois, le sujet peut rester non diagnostiqué. En outre, il n'existe pas de méthodes efficaces et fiables permettant d'établir avec certitude un diagnostic précoce du MP. La difficulté de la détection précoce est une forte motivation pour les outils d'évaluation informatisés/outils d'aide à la décision/instruments de test qui peuvent aider au diagnostic précoce et à la prédiction de la progression de la maladie de Parkinson. La détérioration de l'écriture et la déficience vocale peuvent être l'un des premiers indicateurs de l'apparition de la maladie. Selon la documentation examinée, un modèle indépendant du langage pour détecter la maladie de Parkinson à l'aide de signaux multimodaux n'a pas été suffisamment étudié. L'objectif principal de cette thèse est de construire un système multimodal indépendant du langage pour évaluer les troubles moteurs chez les patients atteints de la maladie de Parkinson à un stade précoce, basé sur des signaux combinés d'écriture et de parole, en utilisant des techniques d'apprentissage automatique. Dans ce but et en raison de l'absence d'un ensemble de données multimodales et multilingues, une telle base de données, également répartie entre les témoins et les patients atteints de la maladie de Parkinson, a d'abord été construite. La base de données comprend des enregistrements de l'écriture, de la parole et des mouvements oculaires recueillis auprès de patients témoins et de patients atteints de la maladie de Parkinson en deux phases (avec médication et sans médication). Dans cette thèse, nous nous sommes concentrés sur l'analyse de l'écriture et de la parole, où les patients atteints de la maladie de Parkinson ont été étudiés avec médication.Des modèles indépendants du langage pour la détection de la maladie de Parkinson basés sur les caractéristiques de l'écriture ont été construits ; deux approches ont été envisagées, étudiées et comparées : une approche classique d'extraction et de classification des caractéristiques et une approche d'apprentissage approfondi. Les deux approches ont permis d'atteindre une précision de classification d'environ 97 %. Un classificateur SVM multi-classes pour la détection des étapes basées sur les caractéristiques de l'écriture a été construit. Les performances obtenues n'étaient pas satisfaisantes par rapport aux résultats obtenus pour la détection de MD en raison des nombreux obstacles rencontrés.Un autre ensemble de caractéristiques acoustiques indépendantes de la langue et de la tâche a été construit pour évaluer les troubles moteurs chez les patients atteints de la maladie de Parkinson. Nous avons réussi à construire un modèle SVM indépendant du langage pour le diagnostic de la MP par analyse vocale avec une précision de 97,62%. Enfin, un système multimodal indépendant du langage pour la détection de la maladie de Parkinson en combinant l'écriture et les signaux vocaux a été mis au point, où le modèle SVM classique et les modèles d'apprentissage profond ont tous deux été analysés. Une précision de classification de 100 % est obtenue lorsque des caractéristiques artisanales des deux modalités sont combinées et appliquées au SVM. Malgré les résultats encourageants obtenus, il reste encore du travail à faire avant de mettre notre modèle multimodal de détection de la MP en usage clinique en raison de certaines limitations inhérentes à cette thèse
Parkinson’s disease (PD) is a neurological disorder caused by a decreased dopamine level on the brain. This disease is characterized by motor and non-motor symptoms that worsen over time. In advanced stages of PD, clinical diagnosis is clear-cut. However, in the early stages, when the symptoms are often incomplete or subtle, the diagnosis becomes difficult and at times, the subject may remain undiagnosed. Furthermore, there are no efficient and reliable methods capable of achieving PD early diagnosis with certainty. The difficulty in early detection is a strong motivation for computer-based assessment tools/decision support tools/test instruments that can aid in the early diagnosing and predicting the progression of PD.Handwriting’s deterioration and vocal impairment may be ones of the earliest indicators for the onset of the illness. According to the reviewed literature, a language independent model to detect PD using multimodal signals has not been enough addressed. The main goal of this thesis is to build a language independent multimodal system for assessment the motor disorders in PD patients at an early stage based on combined handwriting and speech signals, using machine learning techniques. For this purpose and due to the lack of a multimodal and multilingual dataset, such database that is equally distributed between controls and PD patients was first built. The database includes handwriting, speech, and eye movements’ recordings collected from control and PD patients in two phases (“on-state” and “off-state”). In this thesis we focused on handwriting and speech analysis, where PD patients were studied in their “on-state”.Language-independent models for PD detection based on handwriting features were built; where two approaches were considered, studied and compared: a classical feature extraction and classifier approach and a deep learning approach. Approximately 97% classification accuracy was reached with both approaches. A multi-class SVM classifier for stage detection based on handwriting features was built. The achieved performance was non-satisfactory compared to the results obtained for PD detection due to many obstacles faced.Another language and task-independent acoustic feature set for assessing the motor disorders in PD patients was built. We have succeeded to build a language independent SVM model for PD diagnosis through voice analysis with 97.62% accuracy. Finally, a language independent multimodal system for PD detection by combining handwriting and voice signals was built, where both classical SVM model and deep learning models were both analyzed. A classification accuracy of 100% is obtained when handcrafted features from both modalities are combined and applied to the SVM. Despite the encouraging results obtained, there is still some works to do before putting our PD detection multimodal model into clinical use due to some limitations inherent to this thesis

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Munder, Tonia [Verfasser]. « Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder ». Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.

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Munder, Tonia Laura [Verfasser]. « Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder ». Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.

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Konstantopoulos, Konstantinos. « Dysarthria in early Parkinson's disease ». Thesis, University College London (University of London), 2004. http://discovery.ucl.ac.uk/10055767/.

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The aim of the present study was threefold. First, to examine the incidence of dysarthria in patients in the beginning of Parkinson's disease by using a standardised test (Frenchay Dysarthria Assessment/FDA) and an intelligibility assessment tool. Second, to identify differences in speech and in measures of phonation between the Parkinsonian group and a matched control geriatric group using the FDA and electrolaryngography. Finally, to identify the effect of medication on speech and phonation in the dysarthric Parkinsonian group. The results showed that 8 out of 12 (66%) Parkinsonian subjects exhibited lower scores in the FDA compared to controls. Qualitative differences between the two groups were found in the isolated movements of the articulators but not in running speech and speech intelligibility. An improvement in the FDA scoring was found 3-3.5 months after medication. This improvement focused on the areas of tongue and lips and was accompanied with significant increases in intelligibility. No differences in measures of phonation were found either between the two groups or in the same group after medication. The above results suggest that in the beginning of Parkinson's disease, dysarthria is expressed as slowness and may be related to the primary diagnostic symptom of bradykinesia. Due to the small sample and the lack of dosage control, the significance of these findings appears to be inconclusive and warrants further investigation. Future research should employ instrumental quantitative measures on isolated movements of the articulators that may correlate with running speech and will aim to find clinical markers of speech in the diagnosis of Parkinson's disease.

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Kudlicka, Aleksandra Katarzyna. « Executive functioning in early stage Parkinson's disease ». Thesis, Bangor University, 2013. https://research.bangor.ac.uk/portal/en/theses/executive-functioning-in-early-stage-parkinsons-disease(4985b570-fd51-48ba-8c39-f377b5e2edf0).html.

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Background: Cognitive decline is commonly reported in Parkinson’s disease (PD), with some deficits evident even at the onset of PD. Executive functions (EF) are extensively studied in PD and emerge as the domain involving the most profound deficits. Nevertheless, there are some inconsistencies in the literature with regard to the exact pattern of executive deficits and their impact on everyday life in PD. The aim of the literature review presented in this thesis was to synthesise and clarify existing research evidence on EF in early stage PD, and to explore what are the possible factors affecting the consistency of research findings. The empirical studies had three distinct aims: to clarify the pattern of EF deficits in PD; to determine how accurately PwPD appraise potential EF-related difficulties; and to identify how executive deficits impact on people with PD (PwPD) and their families. Method: Studies of EF in PD were systematically reviewed and the findings were synthesised in a series of meta-analyses. Three empirical studies drew on cross-sectional data collected from PwPD and their caregivers, and from healthy older controls. Sixty-five PwPD in mild to moderate stages of PD completed an assessment of EF, awareness, quality of life, and health status, and 43 healthy older controls completed assessment of EF and awareness. Fifty caregivers of PwPD rated the EF of the PwPD and their own burden associated with caring for a PwPD. A sub-group of 34 PwPD, identified as having potential EF deficits, completed a more extensive neuropsychological assessment of executive abilities. Results: The systematic review included 33 studies of EF in early stage PD, and metaanalysis of data from 5 commonly-used tests of EF revealed consistent evidence for executive deficits. The review suggested that the consistency of the research evidence may be improved by more precision in defining EF and more careful selection and interpretation of EF measures. A data-driven analysis examining the pattern of EF impairment distinguished differences between two groups of standard tests of EF, with attentional control tests more frequently compromised than abstract thinking in early stage PD. PwPD were found to be accurate when making general evaluative judgments about their own functioning, but in specific tasks PwPD with executive deficits overestimated their performance in comparison to PwPD without EF deficits and healthy controls. EF-related behavioural difficulties were shown to impact on subjective quality of life in PwPD and on burden in their caregivers. Conclusions: The results of this thesis suggest that EF-related difficulties are frequently present in early stage PD, with attentional control aspects of EF particularly affected, that it may be difficult for PwPD to accurately appraise their own ability to carry out specific activities, and that EF-related difficulties have a significant impact on quality of life in PwPD and their families. A thorough understanding of executive deficits in PD is important in the provision of adequate person-centred care for PwPD and their family members, and could help to inform the development of PD-specific rehabilitative interventions aimed at reducing activity limitation and restrictions on social participation and supporting PwPD in living well with the condition.

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Pursiainen, V. (Ville). « Autonomic dysfunction in early and advanced Parkinson's disease ». Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283888.

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Abstract Parkinson's disease (PD) is known to affect both the extrapyramidal system and the autonomic nervous system even in the early phases of the disease. This study was designed to evaluate cardiovascular autonomic regulation in early PD by measuring heart rate (HR) variability from 24-hour ECG recordings. The dynamics of blood pressure (BP), HR and sweating in patients with and without wearing-off were assessed during clinical observations after a morning dose of levodopa. In patients with wearing-off the tests were repeated after selegiline withdrawal. The power spectral components of HR variability and the SD1 value of the Poincaré analysis that quantifies the short-term beat-to-beat variability were suppressed at night in the PD patients. During the daytime only the SD1 of the Poincaré was suppressed. The results indicate impairment of parasympathetic cardiovascular regulation in untreated patients with PD. The dysfunction was more pronounced at night and in patients with more severe PD. The patients with wearing-off had fluctuation of BP during the observation period, BP increasing when the motor performance worsened and vice versa (p < 0.001). The patients without wearing-off did not show fluctuation of BP. Sweating increased during the observation period, and reached its maximum level at the time of the highest UPDRS motor score phase (off-stage) in patients with wearing-off, but in the patients without wearing-off no changes in sweating were observed. Sweating of the hands was significantly higher in PD patients with motor fluctuations than in those without. Selegiline withdrawal decreased systolic BP significantly during the on-stage in a supine position as well as during the orthostatic test. The initial drop of BP in the orthostatic test was significantly smaller after selegiline withdrawal. The HR and sweating remained unaffected. The results show that the autonomic nervous system is affected in the early phases of PD. The dysfunction becomes more pronounced with the disease progression. Wearing-off type motor fluctuations are associated with fluctuation of BP and sweating and these fluctuations may represent autonomic dysfunction caused by PD, the effect of PD medication, or both. Selegiline withdrawal seems to alleviate the orthostatic reaction in patients with advanced PD.

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Szewczyk-Krolikowski, Konrad. « Clinical and imaging characteristics of early Parkinson's disease ». Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c118f620-19a9-4d0c-bcfc-018e3dd9ff3d.

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Background. Pathological processes in Parkinson’s disease (PD) start long before the first symptoms appear and by the time the disease is clinically established the results of neurodegeneration may be irreversible. Efforts to prevent or stem disease progression need to start in early disease and good characterization and new markers of early PD are urgently needed. Objectives. This thesis aims to characterize early disease stages in three projects. Firstly, clinical features of PD within 3 years of diagnosis will be explored in an incident cohort of patients and controls, using a range of tools to cover the whole breadth of clinical presentation of PD. Secondly, functional imaging studies in PD published so far will be examined through a meta-analysis to identify the most robust functional imaging markers. Thirdly, a functional MRI resting-state study in early PD will be performed to identify reproducible differences between patients and matched control subjects. Results. The cohort analysis found that age was a strong predictor of disease severity, independent of disease duration, while gender was seen to affect disease severity depending on the body region. A meta-analysis of all published functional imaging studies across all disease stages showed abnormal activations in the Basal Ganglia but also in a wide range of motor and non-motor brain areas. Dopamine supplementation normalized activations in the Basal Ganglia and some other areas, while other circuits remained resistant to medication suggesting non-dopaminergic abnormality. In the resting-state study, the Basal Ganglia Network showed greatly reduced connectivity in early PD compared to controls, which normalized on administration of dopaminergic medication. Reduced BGN connectivity was also validated on a separate group of PD subjects achieving very good separation of patients from controls. Conclusions. The effect of gender and age on early presentation of PD has potential significance for early diagnosis and choice of outcome measures for clinical trials. Within the realm of imaging, traditional task-based fMRI studies fail to show a clear and reproducible pattern of activations making this method unfeasible for early diagnostic testing. In contrast, resting-state fMRI connectivity in the Basal Ganglia Network appears to be a promising and reliable method even in the early stages of PD. Clinical profiling and resting imaging changes offer avenues for developing future biomarkers in early PD.

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Saad, Ali. « Detection of Freezing of Gait in Parkinson's disease ». Thesis, Le Havre, 2016. http://www.theses.fr/2016LEHA0029/document.

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Le risque de chute provoqué par le phénomène épisodique de ‘Freeze of Gait’ (FoG) est un symptôme commun de la maladie de Parkinson. Cette étude concerne la détection et le diagnostic des épisodes de FoG à l'aide d'un prototype multi-capteurs. La première contribution est l'introduction de nouveaux capteurs (télémètres et goniomètres) dans le dispositif de mesure pour la détection des épisodes de FoG. Nous montrons que l'information supplémentaire obtenue avec ces capteurs améliore les performances de la détection. La seconde contribution met œuvre un algorithme de détection basé sur des réseaux de neurones gaussiens. Les performance de cet algorithme sont discutées et comparées à l'état de l'art. La troisième contribution est développement d'une approche de modélisation probabiliste basée sur les réseaux bayésiens pour diagnostiquer le changement du comportement de marche des patients avant, pendant et après un épisode de FoG. La dernière contribution est l'utilisation de réseaux bayésiens arborescents pour construire un modèle global qui lie plusieurs symptômes de la maladie de Parkinson : les épisodes de FoG, la déformation de l'écriture et de la parole. Pour tester et valider cette étude, des données cliniques ont été obtenues pour des patients atteints de Parkinson. Les performances en détection, classification et diagnostic sont soigneusement étudiées et évaluées
Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of Parkinson's disease (PD). This research is headed toward implementing a detection, diagnosis and correction system that prevents FoG episodes using a multi-sensor device. This particular study aims to detect/diagnose FoG using different machine learning approaches. In this study we validate the choice of integrating multiple sensors to detect FoG with better performance. Our first level of contribution is introducing new types of sensors for the detection of FoG (telemeter and goniometer). An advantage in our work is that due to the inconsistency of FoG events, the extracted features from all sensors are combined using the Principal Component Analysis technique. The second level of contribution is implementing a new detection algorithm in the field of FoG detection, which is the Gaussian Neural Network algorithm. The third level of contribution is developing a probabilistic modeling approach based on Bayesian Belief Networks that is able to diagnosis the behavioral walking change of patients before, during and after a freezing event. Our final level of contribution is utilizing tree-structured Bayesian Networks to build a global model that links and diagnoses multiple Parkinson's disease symptoms such as FoG, handwriting, and speech. To achieve our goals, clinical data are acquired from patients diagnosed with PD. The acquired data are subjected to effective time and frequency feature extraction then introduced to the different detection/diagnosis approaches. The used detection methods are able to detect 100% of the present appearances of FoG episodes. The classification performances of our approaches are studied thoroughly and the accuracy of all methodologies is considered carefully and evaluated

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Possin, Katherine L. « Visuospatial and visual object cognition in early Parkinson's disease ». Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3250074.

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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2007.
Title from first page of PDF file (viewed April 4, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 128-166).

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Sullivan, Kelly. « Indicators of Early Adult and Current Personality in Parkinson's Disease ». Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3371.

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Introduction: Previous epidemiologic studies suggest that the personality of Parkinson's disease (PD) patients differs from that of controls, and laboratory evidence supports a potential common pathophysiology of personality traits and PD. One nested case-control study found that PD cases were significantly more anxious than controls before the clinical onset of the disease, and additional data suggest that certain occupations may be risk factors for the disease. Additionally, the latent period that precedes the onset of motor symptoms of PD is unknown. Objectives: The objectives of this study were to evaluate the association of PD with objective indicators of current and pre-morbid personality, to determine the correlation of early-adult life personality indicators with current personality characteristics and to evaluate the role of personality as indicated by occupational choice and employment patterns in the risk for PD using the Dictionary of Occupational Titles job classification system. Methods: Eighty-nine cases and 99 controls completed in-person structured interviews. Assessments included measures of current personality characteristics and indicators of early-adult (ages 20-35 years) personality, such as activities and lifestyle patterns. Associations between these latent personality variables and current personality characteristics were studied using correlation, partialling out the effects of age, sex and education. Multiple logistic regression was used to evaluate the associations of early-adult personality and occupational characteristics and the risk for Parkinson's disease. Results: Cases with Parkinson's disease reported higher levels of neuroticism (OR=1.05 (95% CI 1.00-1.11)) and harm-avoidance (OR = 1.07 (95% CI 1.00-1.15)) compared with controls on measures of current personality. A stable association among many traits, particularly traits such as novelty-seeking, which are driven by dopaminergic function, was present not only among controls with presumably normal dopaminergic function throughout their lives, but also among cases. Early-adult life routinization was correlated with current levels of neuroticism (cases: r=0.33, p=0.01; controls: r=0.26, p=0.04), extraversion (cases: r=-0.33, p=0.01; controls: r=-0.33, p=0.04), novelty-seeking (cases: r=-0.33, p=0.015; controls: r=-0.34, p=0.007) and harm-avoidance (cases: r=0.47, p=0.0003; controls: r=0.45, p=0.0002) and for the association of early-adult life activity risks with harm-avoidance (cases: r=-0.47, p=0.0004; controls: r=-0.42, p=0.0006). Taking or wanting to take "activity risks," such as riding on roller coasters as a young adult was found to reduce the odds of Parkinson's disease (OR = 0.78 (95% CI 0.63-0.97)) in the entire sample, while higher levels of early-adult routinization were associated with a greater risk for Parkinson's disease among women (OR=1.63 (95% CI 1.05-2.53)). Parkinson's disease was inversely associated with the total number of jobs held (OR=0.87 (95% CI 0.75-0.99)) but not with the number of job categories or duration of the primary occupation. Increased complexity of work with people was associated with PD among women (OR=0.69 (95% CI 0.53-0.89), as was less complex work with things (OR=1.45 (95% CI 1.11-1.88). The complexity of work with data, people or things was not associated with the risk for PD among men or in the sample as a whole. Men with PD whose work involved greater complexity with data took fewer activity risks (r=0.32, p=0.02) and reported greater routinization (r=-0.34, p=0.01) as a young adult. Conclusions: This evaluation of early-adult risk factors advances current knowledge about the premorbid period of PD and supports the hypothesis that a long period of subclinical disease precedes the onset of motor symptoms. These findings validate the association of these early-adult personality traits and PD and support the idea that behaviors associated with PD personality exist many years before the presentation of motor symptoms. Dopaminergic aspects of personality were related to occupational choices and future consideration of this hypothesis is warranted. Since PD is a degenerative disorder, determining the age of onset of this illness is important in the search for modifiable risk factors and neuroprotective strategies.

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Chamoun, Mario-Christofer. « An Alzheimer-type cerebrospinal fluid profile in early Parkinson's disease ». Thesis, Umeå universitet, Institutionen för psykologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-167374.

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In recent years, several studies have discovered traces of Alzheimer's (AD) biomarkers in a large portion of patients with Parkinson's disease (PD), which have been associated with subsequent dementia (PDD). However, the manifestation of AD biomarkers in PD is not fully understood. At present, few studies have investigated how common AD biomarkers are in newly diagnosed and unmedicated patients with PD. This cross-sectional cohort study investigated whether AD biomarkers were present in unmedicated and newly diagnosed patients with PD and patients with PD and overlapping clinical symptoms (cognitive impairment, depression, olfactory dysfunction). Cerebrospinal fluid (CSF) levels of AD biomarkers Amyloid-β-42 (Aβ42), phosphorylated-tau (p-tau), and total-tau (t-tau) were assessed in 343 patients with the mean age of 68,69 (SD=9,60), including 31 healthy controls with the mean age of 68,90 (SD=5,64). The participants were recruited from The New Parkinson Patient in Umea (NYPUM & PARKNY). The results showed a significant difference in CSF AD biomarkers between patients with PD and healthy controls, but not in patients with PD and overlapping clinical symptoms. The results point to the presence of AD pathology in early PD; however, the presence of AD pathology could not be further strengthened by the clinical overlapping symptoms. More prospective studies on newly diagnosed patients with PD need to be carried out to investigate the prognostic values of the presence of AD pathology found in PD.

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Malek, Naveed. « Variation in Parkinson's disease : age, gender, genotype and phenotype correlations in early onset disease ». Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/5602/.

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There is a wide variation in the phenotypic expression, progression rates, therapy response and complications in Parkinson’s disease (PD). The primary research objective in this thesis was to analyse the variation in the 4 domains of phenotypic expression i.e. motor, non-motor, cognitive, and quality of life in a subset of early onset Parkinson’s disease (EOPD) patients from the PRoBaND study, in the United Kingdom. The secondary objective was to explore the factors responsible for this variation or heterogeneity in the clinical characteristics. Linking genotypes with phenotypes, besides evaluating environmental risk factors and iatrogenic influences, represents one mechanism of understanding this variation in the phenotypic expression of PD. We found subtle but significant variation across all domains of symptoms examined in this thesis by classifying patients into groups according to motor subtype, gender, age at diagnosis and heritability of the parkinsonian trait, despite statistically insignificant differences in risk factors such as head trauma, exposure to pesticides (including herbicides, insecticides, fungicides and fumigants), heavy metals, caffeine and a past history of oophorectomy (in females) with the exception of smoking (p=0.046) and exposure to solvents, which were more common in males compared to females (p<0.001). There were differences in the prevalence of motor symptoms such as balance problems being more prevalent in the postural instability gait difficulty (PIGD) subtype compared to the tremor dominant PD (TDPD) and ‘Mixed’ motor subtypes both subjectively (p<0.001) and objectively (p<0.001). Other axial problems such as speech difficulties and freezing were also more prevalent in those with the PIGD phenotype compared to the other motor subtypes both subjectively (p=0.004, p<0.001) and objectively (p=0.002, p<0.001). There was also variation in the prevalence of motor complications such as dyskinesia (p<0.001) and dystonia (p=0.020), being more prevalent in the PIGD subtype compared to other motor subtypes. 8 The prevalence of certain non-motor symptoms such as pain (p=0.022) and features of gastrointestinal dysfunction e.g. prandial bloating (p=0.024) and constipation (p=0.022) were more commonly reported by females compared to males. There were also differences in the prevalence of cognitive impairment (p=0.049) and neurobehavioural characteristics such as anxiety (p=0.002) and depression (p=0.006), after the diagnosis of PD, being more prevalent in PIGD compared to other motor subtypes. Finally, these differences contributed to the variation in the independence of activities of daily living scores which were lower in those with the PIGD phenotype compared to other motor subtypes (p<0.001). There were some differences in exposure to environmental risk factors for PD but not sufficient to explain all the variation. Iatrogenic influences from drugs contributed in part to the phenotypic variation. 10% of the cases in the EOPD cohort tested positive for mutations in one of three genes screened i.e. LRRK2, GBA and Parkin; their DNA remains banked and there is scope to test these cases for mutations in other genes, relevant to PD, in the future. There were too small numbers of cases in each subgroup to draw definite conclusions about the exact influence of genes on the overall phenotypic variation but differences between Parkin mutation carriers and gene test negative ‘controls’ such as early age of onset and long disease duration were obvious. PRoBaND is linked to other similar research studies in the UK, with the stated aim of sharing datasets, in the hope that larger numbers of patients and their DNA samples will increase the power, in statistical terms, to test hypotheses about the role of genetic markers in influencing the course and expression of symptoms. Our current understanding of PD as a complex trait suggests both genetic and environmental influences (including iatrogenic factors if patients are treated) play a role in the phenotypic expression of this condition. A lot more remains to be explored to improve our understanding of the finer details and molecular mechanisms underlying the variation in this disease.

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Domellöf, Magdalena Eriksson. « Cognitive and motor dysfunction in the early phase of Parkinson's disease ». Doctoral thesis, Umeå universitet, Institutionen för farmakologi och klinisk neurovetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-82897.

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Background: Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disease. The diagnosis is based on a combination of the motor signs: tremor, bradykinesia, rigidity and postural abnormalities. Mild Cognitive Impairment (MCI) is common early in the disease and a large proportion of patients with PD develop dementia (PDD). Associations between motor symptoms and cognitive decline have been suggested but the results are inconclusive due to differences in the selection of participants and variables tested. Large population based studies with comprehensive neuropsychological investigation in newly diagnosed cases with PD followed prospectively are rare. The aim of this thesis was to improve characterization and understanding of cognition in PD, and to explore the relationship to motor impairment in the early phase of PD. Methods: All new patients with suspected idiopathic parkinsonism in the catchment area (142 ooo inhabitants) were examined during a period of five years and four months. Among other investigations, a comprehensive neuropsychological evaluation was carried out in 119 of 148 patients with PD together with 30 age matched healthy controls. Assessments were repeated after one three and five years. Results: Patients performed worse than healthy controls in a majority of neuropsychological tests. MCI at the time of diagnosis were found in 36% according to recently published MCI criteria. Thirty % were cognitively impaired using another definition. One fourth of the patients developed PDD within five years after diagnosis and 25 % of those with MCI at baseline reversed back to normal cognition. Age and MCI were significant predictors of dementia. Education was an independent predictor for severe cognitive dysfunction at diagnosis but did not predict PDD. Patients with MCI converting to PDD had worse performance on visuospatial function, semantic fluency, episodic memory, mental flexibility and conceptual thinking. There were no differences in cognitive performance between patients with predominant Postural and Gait Disturbances (PIGD) and the tremor dominant subtype at the baseline investigation and belonging to the PIGD subgroup at baseline did not predict PDD. Dementia converters declined more rapidly than non-converters in posture/gait function. Associations between bradykinesia and measures of executive functions and working memory were found, and between posture and gait disturbances and visuospatial function. Some of these associations were persistent after one year. Patients receiving the dopamine agonist pramipexole performed significantly worse on a measure of verbal fluency at the one year follow up. Conclusions: The differences in proportions of cognitively impaired in the different studies emphasize the value of joint criteria for PD-MCI. Even when using such criteria, a substantial proportion of patients revert back to normal function. The increase in motor disability in patients with PDD could have several different causes that need to be further investigated. Associated motor and cognitive dysfunctions could reflect common pathophysiological processes in partly shared networks. Both dopaminergic and non-dopaminergic motor and cognitive functions seems to be involved in PDD which suggests that pharmacological treatment in PD needs to go beyond the scope of dopaminergic deficiency in search for new therapies that would also be effective for non-motor symptoms.
NYPUM

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Ho, Arthur Yau Wing. « Recommendation for using deep brain stimulation in early stage Parkinson's disease ». Thesis, Boston University, 2013. https://hdl.handle.net/2144/21175.

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Thesis (M.A.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
Parkinson's disease is a progressively debilitating disease that affects about 1% of the world's population, and does not differentiate between genders or races. The disease is caused by the death of the dopaminergic neurons in the basal ganglia nuclei, especially those in the substantia nigra pars compacta. Subsequent loss of dopamine production engenders the cardinal symptoms of bradykinesia, rigidity, akinesia, and postural instability found in all patients with Parkinson's disease. While there are several types of Parkinson's disease, the majority of the cases are made up of the idiopathic and Levodopa responsive type. The current consensus on treatment is to use medications until the patient becomes refractory to all medicines. It is only at this point will the surgical option deep brain stimulation be considered. while this procedure comes with a higher risk of post surgery complications, the benefits it offers patients with advanced Parkinson's disease are far superior to those offered patients by medications. It reasons then that patients would benefit more if they received this treatment earlier in the course of the disease. The mechanisms, side effects, costs, cost-effectiveness, and long term effects on quality of life of deep brain stimulation will be compared with those of medications to assess whether it is worthwhile to use this treatment for patients with mild Parkinson's disease.
2031-01-01

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Chen, Lei [Verfasser]. « Computer-aided detection of Parkinson's Disease using transcranial sonography / Lei Chen ». Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1046712691/34.

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Page, Brent Michael. « Study of an Early Wellness Program in Parkinson ’s Disease : Impact On Quality Of Life And Early Intervention Guidance ». Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/623632.

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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.
Previous studies have shown that Parkinson’s disease (PD) patients are at an increased risk for a variety of complications impacting health related quality of life (HRQoL). Additionally, these various complications often lead to increased healthcare utilization. Wellness intervention in PD has shown to be effective in improving HRQoL and objective measures of disease burden such as motor functioning. What has not been demonstrated to date is whether patients who are given the opportunity to participate in regularly administered classes in these modalities will continue to attend and whether benefits will continue to be realized outside the strict confines of a controlled trial. This study examined whether intervening early in PD with a comprehensive Wellness Program is feasible and promotes lasting habits that will continue to provide sustained benefit. It was hypothesized that intervening early in PD with an intensive program involving structured exercise, socialization and PD specific education would serve to maintain or improve subject’s quality of life while decreasing healthcare utilization. Twenty‐one consenting ambulatory adult subjects diagnosed with PD within the last five years completed various screenings at baseline and following a required 6‐month Wellness Program intervention. Subjects were assessed at 12 and 18 months if they continued to participate. Patient demographics, disease specific quality of life, objective mobility, healthcare utilization and falls were assessed. Data were collected at Banner Sun Health Research Institute, located in Sun City, Arizona. All p‐values were 2‐tailed and P<0.05 was considered statistically significant. All data analyses were conducted using STATA‐14. Twenty of twenty‐one subjects completed the required 6‐month intervention. Continued participation was 70% at 12 months and 60% at 18 months. Overall HRQoL was stable at 18 months. Significant improvement was seen in patient reported mobility and emotion sub‐areas at 12 months. Communication specific HRQoL was significantly worsened at 12 months. Subjects demonstrated a stable level of physical activity while fatigue was significantly decreased. All objective measures were significantly improved from baseline. Healthcare utilization was decreased by 18 months. A total of 5 falls were reported by 3 subjects during the 6‐month interventional period. This pilot study demonstrates that comprehensive wellness intervention in early PD is feasible, effective, safe and valuable in establishing long‐term beneficial habits while potentially reducing healthcare utilization. The significant long‐term subject participation observed in this study establishes that wellness intervention may be practical for large scale implementation. The results also highlight the importance of addressing communication specific symptoms early in the course of the disease. Ultimately, this study will aid the design and implementation of future PD wellness interventions.

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Lui, Nga Ping. « Endogenous neuroprotective mechanisms in early stages of rat parkinsonism ». HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1251.

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Conrado, Daniela J., Timothy Nicholas, Kuenhi Tsai, Sreeraj Macha, Vikram Sinha, Julie Stone, Brian Corrigan et al. « Dopamine Transporter Neuroimaging as an Enrichment Biomarker in Early Parkinson's Disease Clinical Trials : A Disease Progression Modeling Analysis ». WILEY, 2018. http://hdl.handle.net/10150/626602.

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Given the recognition that disease-modifying therapies should focus on earlier Parkinson's disease stages, trial enrollment based purely on clinical criteria poses significant challenges. The goal herein was to determine the utility of dopamine transporter neuroimaging as an enrichment biomarker in early motor Parkinson's disease clinical trials. Patient-level longitudinal data of 672 subjects with early-stage Parkinson's disease in the Parkinson's Progression Markers Initiative (PPMI) observational study and the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) clinical trial were utilized in a linear mixed-effects model analysis. The rate of worsening in the motor scores between subjects with or without a scan without evidence of dopamine transporter deficit was different both statistically and clinically. The average difference in the change from baseline of motor scores at 24 months between biomarker statuses was -3.16 (90% confidence interval [CI] = -0.96 to -5.42) points. Dopamine transporter imaging could identify subjects with a steeper worsening of the motor scores, allowing trial enrichment and 24% reduction of sample size.

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Stephens, Aubree. « Non-motor symptoms and their use as markers for prodromal and early Parkinson's disease ». Thesis, Uppsala universitet, Institutionen för organismbiologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445208.

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Parkinson’s Disease (PD) is the second most common neurodegenerative disorder. It is a disease with a broad spectrum of symptoms, both motor and non-motor, but is often only diagnosed when the motor symptoms begin to appear. By this time however, a large amount of the dopaminergic neurons of the substantia nigra pars compacta have already deteriorated. It is therefore of great interest to be able to diagnose the disease earlier on in its progression and perhaps slow down or halt its course. Recent literature has supported the idea that non-motor symptoms begin to appear years, perhaps even decades, before the motor symptoms are visible. This makes them a prime candidate for diagnosing PD earlier on. With the aim of assessing the prevalence of different NMS in prodromal and early Parkinson’s, 19 studies addressing different NMS were analyzed. It was found that NMS are prevalent in both prodromal and early PD. The strongest prodromal predictors for PD were found to be olfactory dysfunction and REM-sleep behavior disorder (RBD).

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Heard, Stephanie. « Plant pathogen sensing for early disease control ». Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/plant-pathogen-sensing-for-early-disease-control(48949f80-2596-4ce2-912a-6513e72f6a8d).html.

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Sclerotinia sclerotiorum, a fungal pathogen of over 400 plant species has been estimated to cost UK based farmers approximately £20 million per year during severe outbreak (Oerke and Dehne 2004). S. sclerotiorum disease incidence is difficult to predict as outbreaks are often sporadic. Ascospores released from the fruiting bodies or apothecia can be dispersed for tens of kilometres. This makes disease control problematic and with no S. sclerotiorum resistant varieties available, growers are forced to spray fungicides up to three times per flowering season in anticipation of the arrival of this devastating disease. This thesis reports the development of the first infield S. sclerotiorum biosensor which aims to enable rapid detection of airborne ascospores, promoting a more accurate disease risk assessment and fungicide spraying regime. The sensor is designed to detect the presence of oxalic acid, the main pathogenicity factor secreted during early S. sclerotiorum ascospore germination. Upon electrochemical detection of this analyte in the biosensor, a binary output is relayed to farmer to warm him of a disease risk. This project focused on the development of a nutrient matrix which was designed to be contained within the biosensor. The role of this matrix was to promote the growth of captured airborne S. sclerotiorum ascospores and induce high levels of oxalic acid secretion. The use of the designed biological matrix to promote oxalic acid production was tested during three field trials in S. sclerotiorum artificially inoculated fields. This thesis describes the use of contemporary pathogenomics technologies to further investigate candidate genes involved in pathogenicity alongside the secretion of oxalic acid. A pre-described bioinformatics pipeline was used to predict the S. sclerotiorum secretome to identify potential effector proteins as well as explore proteins which are unique to S. sclerotiorum to be used as other novel targets for detection. GFP tagged constructs were designed to investigate the expression of the putative targets for S. sclerotiorum detection. The transcriptomes of wild type and oxalic acid deficient S. sclerotiorum strains during infection as well as during a saprotrophic stage were investigated. This study provided expression support for not only some of the unannotated genes identified in the putative secretome, but some candidate genes speculated to be involved in infection.

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Mendel, Julian L. « Laurel Wilt Disease : Early Detection through Canine Olfaction and "Omics" Insights into Disease Progression ». FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3475.

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Laurel wilt disease is a vascular wilt affecting the xylem and water conductivity in trees belonging to the family Lauraceae. The disease was introduced by an invasive species of ambrosia beetle, Xyleborus glabratus. The beetle, together with its newly described fungal symbiont Raffaelea lauricola (pathogenic to host trees), has lead to the devastation and destruction of over 300 million wild redbay trees in southeastern forests. Ambrosia beetles make up a very unique clade of beetle and share a co-evolved obligatory mutualistic relationship with their partner fungi. Rather than consuming host tree material, the beetles excavate galleries or canals within them. These galleries serve two purposes: reproduction and fungal gardening. The beetles house fungal spores within specialized sacs, mycangia, and essentially inoculate host trees with the pathogenic agent. They actively grow and cultivate gardens of the fungus in galleries to serve as their sole food source. Once the fungus reaches the xylem vessels of the host tree, it thrives and leads to the blockage of water flow, both because of fungal accumulation and to the host response of secreting gels, gums and tyloses to occlude vessels in an attempt to quarantine the fungus. This disease spreads rapidly, and as a result, once symptoms become visible to the naked eye, it is already too late to save the tree, and it has likely already spread to adjacent ones. The present study presents the first documented study involving the early detection of disease from deep within a tree through the use of scent-discriminating canines. In addition, the present study has lead to the development of a novel sample collection device enabling the non-destructive sampling of beetle galleries. Finally, a metabolomics approach revealed key biochemical pathway modifications in the disease state, as well as potential clues to disease development.

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Buckley, Christopher. « Upper body accelerations as a biomarker of gait impairment in the early stages of Parkinson's disease ». Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19779/.

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Neurodegenerative diseases such as Parkinson's disease (PD) impair the ability to walk safely and efficiently. Currently, clinical rating scales designed to assess gait are often described to be subjective and lacking sensitivity to detect gait impairments at the early stage of the disease. Devices are available to objectively measure gait within research laboratories; however, they are often expensive and require trained expertise. Inertial measurement units (IMUs) may be an ideal device to measure gait while overcoming many of the limitations of other devices. They can measure movements of the upper body, which in PD is known to be impaired, and therefore may enable the calculation of a variety of acceleration based variables better capable to quantify impaired gait in PD. This thesis aimed to determine the ability of a variety of acceleration based variables obtained from different location of the upper body to detect movements symptomatic to PD from age matched controls. Variables yet to be applied to PD were tested and methodological reasons for why differing results found in the literature was analysed, in an attempt to develop a refined methodology specific to PD. Acceleration based variables were tested relative to, and combined with, variables obtained from a 7m pressure sensitive mat. It was tested whether these variables bring additional information about a patient's gait or if they are merely a reflection of lower limb mechanics, and, whether they can classify PD gait independently or in combination with a pre-existing spatiotemporal model of gait. Results showed that for a large population of people with early stage PD, upper body acceleration variables not previously applied to PD were capable to highlight gait impairments. However, attention must be made to the processing of the acceleration signals as the method used to realign signals to a global reference can significantly impact a variable's sensitivity to PD. Lastly, it was shown that the majority of upper body acceleration variables are unique from typically measured spatiotemporal information, and when using a multivariate approach, were equally capable to highlight gait impairment in PD. This thesis therefore proposes that variables calculated from the upper body using IMUs can be useful biomarkers of gait impairment at the early stage of PD, and if possible, should be used in conjunction with traditional approaches.

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Geng, Jiao [Verfasser], et Jochen [Akademischer Betreuer] Herms. « Early changes in miRNAs in a mouse model of Parkinson's Disease / Jiao Geng ; Betreuer : Jochen Herms ». München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1226092357/34.

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Sarini, Sarini. « Statistical methods for modelling falls and symptoms progression in patients with early stages of Parkinson's disease ». Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116208/1/_Sarini_Thesis.pdf.

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This thesis was a step forward in gaining insight into falls in people with early stages of Parkinson's disease (PD), and in monitoring the disease progression based on clinical assessments. This research contributes new knowledge by providing new insights into utilizing information provided by the clinically administered instruments used routinely for the assessment of PD severity. The novel approach to modelling the progression of PD symptoms using multi-variable clinical assessment measurements for longitudinal data provides a new perspective into disease progression.

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Olsson, Louise. « Early detection of colorectal cancer / ». Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-841-6/.

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Hurrell, Karen Tracy. « Screening for serious disease : modelling the early detection of breast cancer ». Thesis, University of Leicester, 1989. http://hdl.handle.net/2381/34546.

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Sriram, Deepa. « Early detection of bowel disease in symptomatic patients attending community pharmacies ». Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/510.

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With evidence that a substantial number of individuals manage symptoms without seeking medical help, a pharmacy service with symptom management guided by a screening tool should assist in the triage of clients with underlying health conditions. This research describes the successful development, validation and testing of an assessment questionnaire to guide pharmacy staff in identification of pharmacy clients at risk of serious bowel conditions, with referral of clients for further investigation where warranted.

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Khan, Naheed Lubna. « Familial parkinsonism (Parkinson's disease and early onset parkinsonism) : a genetic, clinical study and 18F-dopa PET study ». Thesis, University College London (University of London), 2006. http://discovery.ucl.ac.uk/1444816/.

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This thesis reports a study of familial parkinsonism using diverse scientific tools of both molecular genetics and positron emission tomography functional imaging. Population-based allelic association studies are also described. Molecular and clinical characterisation of the largest British kindred with autosomal Parkinson's disease (PD). Genome wide linkage analysis was used to map the sub-chromosomal location of the disorder as the first step for disease-gene identification. The disease mapped to a 50cM region on the short arm of chromosome 12 overlapping a locus, PARK8 that had just been reported in a Japanese kindred, with an identical phenotype. A detailed clinical study of the British kindred identified unilateral onset of tremor in the leg, prominent foot dystonia and behavioural disorder. Intact cognition and sustained levodopa response, was observed despite lengthy disease duration. Phenotypic study of PARK2 / parkin disease This case series reports a detailed clinical assessment of twenty four cases of early onset parkinsonism with mutations in PARK2, emphasising the clinical features, atypical phenotypes including cervical dystonia, autonomic dysfunction, peripheral neuropathy, pure exercise-induced dystonia and behavioural disorder prior to the onset of parkinsonism. Olfaction was normal compared to a group of parkin negative patients and idiopathic PD cohorts. A number of unaffected relatives who were parkin heterozygotes had psychiatric symptoms and some had extrapyramidal signs that did not fulfil Queen Square Brain Bank criteria. 18F-dopa Positron Emission Tomography in Familial Parkinsonism. Functional imaging in cohorts of patients with parkin (PARK2) and PINK1 (PARK6) mutations identified patterns of nigrostriatal dysfunction that was bilateral and uniform unlike that seen in idiopathic PD. Serial 18F-dopa PET used to assess disease progression in parkin disease over a ten year period, showed that the rate of loss of dopaminergic function was slower compared to idiopathic PD. Asymptomatic parkin and PINK1 heterozygotes also had nigrostriatal dysfunction implying that the gene products exhibited 'haploinsufficiency'. Population based Allelic Association studies. A large study using pathologically proven PD cases and controls failed to replicate a report of positive association between an alpha synuclein polymorphism and PD. In a separate study the angiotensin converting enzyme gene was used as a candidate disease gene. Familial parkinsonism encompasses a heterogeneous group of diseases. Familial 'parkinsonism' was observed in early onset, recessive disease with atypical phenotypes, normal smell and patterns of nigrostriatal dysfunction and rate of progression of functional imaging unlike that seen in idiopathic PD. Functional imaging in asymptomatic heterozygotes suggested that parkin and PINK1 proteins exhibited the phenomenon of haploinsufficiency. Familial Parkinson's disease, however, with a typical phenotype and pattern of 18F-dopa uptake similar to PD was observed in an autosomal dominant British kindred. This thesis also confirmed locus heterogeneity in autosomal dominant Parkinson's disease and studied a putative susceptibility allele, ACE, in a population-based study.

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Cirnaru, M. D. « IMPACT OF LRRK2 KINASE ACTIVITY AT THE PRE-SYNAPTIC SITE : EARLY AND LATE EFFECTS ON PARKINSON'S DISEASE ». Doctoral thesis, Università degli Studi di Milano, 2016. http://hdl.handle.net/2434/353078.

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Parkinson’s disease (PD) is a common neurodegenerative disease clinically characterized by bradykinesia, rigidity and resting tremor. PD is characterized pathologically by the degeneration of nigrostriatal dopaminergic neurons and the presence of Lewy bodies containing a small protein, alpha-synuclein. Mutations in Leucine-rich repeat kinase 2 gene (LRRK2) are associated with familial and sporadic Parkinson’s disease (PD). LRRK2 is a complex protein that consists of multiple domains executing several functions, including GTP hydrolysis, kinase activity, and protein binding. There are many single nucleotide alterations covering LRRK2's functional domains, but the main missense mutations that clearly segregate with PD in large family studies, cluster within the enzymatic domains. The G2019S mutation falls in the activation loop of the kinase domain generating a 2-fold increase in LRRK2 kinase activity. Although patients with LRRK2 mutations usually respond to levodopa therapy, this treatment is only symptomatic and it does not cure the cause of the disease. In particular, LRRK2 mutations lead to neuronal cell death and toxic protein aggregates and LRRK2 kinase activity seems to be responsible for the observed neurotoxicity. Our previous research pointed out that LRRK2 acts at the presynaptic site where interacts with synaptic vesicles (SV) and presynaptic proteins together with which it controls SV trafficking in a kinase depended manner. Our recent data indicate NSF not only as an interactor but also as a substrate for LRRK2 kinase activity in vitro. In the present work we analyze how LRRK2 increased kinase activity conferred by the PD related G2019S mutation influences the neuronal functions. We investigated whether G2019S mutation might affect presynaptic function in short term and substrate clearance in long term. Next, we evaluated the feasibility of two potential therapeutic strategies: the first implies the use of LRRK2 kinase inhibitors while the second focuses on treatment ameliorating protein degradation via induction of autophagy. By dynamic studies of SV release in cultured neurons of human LRRK2 G2019S (GS) overexpressing mice and in breed wild-type mice, we found that the increase of LRRK2 kinase activity positively correlates with an increase in the endocytosis rate of the SV. Moreover, we report also an impairment in the complexity of the neuronal tree of the GS neurons that depends on both increased protein level and kinase activity. We recently reported that LRRK2 phosphorylates NSF at threonine 645 inducing an increase in NSF’s ATP hydroxylation rate that determine an increase in the SNARE disassembly. Moreover, we found that aged GS mice show aberrant NSF protein accumulation and motor as well as cognitive impairment. We report also that the chronic treatment with trehalose, an autophagy inducing molecule, partially recovered the motor phenotype and NSF aggregation proposing it as an interesting therapeutic strategy.

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Biba, Matilda. « Colour vision within occupations and in the early detection of retinal disease ». Thesis, City University London, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654960.

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Colour is used extensively in the railway industry to enhance conspicuity, code information and group objects of interest together. An acceptable level of colour discrimination is therefore required to enhance visibility even when colour is used redundantly, thus justifying the need to adequately screen all employed personnel. Rail Safety and Standards Board (2002, 2007) state that colour vision must be normal, as assessed by the Ishihara Plates Test or City University test. However, current conventional screening tests are limited in design and have high variability. As a result, a study was commissioned by Transport for London (TfL) to derive empirically, new limits of colour vision loss that could be classed as safe for train operators, within the London Underground (LU) environment. An initial visual task analysis was conducted to indentify the most safety-critical, colour-related task that LU train drivers encountered. This was determined to be the 'within tunnel' signal lights. Unique to this study was the original and faithful laboratory construction and reproduction of the within-tunnel signal light task which made it possible to establish experimentally new colour limits. A total of 100 subjects were recruited for the study, 40 normal trichromats and 60 congenital colour deficient'. They were assessed on conventional screening tests, the Colour Assessment and Diagnosis (CAD) test and compared against the simulated Trains Light (TL) test. Safe colour vision limits were established for two approach distances of 220 m and 110 m. Analysis indicated that for an approach distance of 110 m, deuteranomalous subjects with RG CAD thresholds < 7 and protanomalous subjects with RG CAD thresholds < 10 performed the TL test as well as normal trichromats and were therefore classed as safe to operate a train. An extended analysis of the proposed limits which included previous study data and LU data was conducted. A total of 606 subjects' data was reviewed (normal n = 205, deutan n = 269, protan n = 132) to provide accurate predictive outcomes. Based on the proposed 110 m limits, - 40% of colour deficient applicants would be classed as safe to drive a train. If the approach distance of 220 m were adopted, - 11 % of colour deficient applicants would be classed as safe to conduct a train based on stricter RG CAD thresholds limits of < 2.5 for deuteranomalous subjects and < 9 for protanomalous subjects. TfL have now accepted the CAD limits proposed for an approach distance of 110 m, classifying - 40% of the colour deficient population as safe. The CAD system now replaces the Ishihara test in the LU occupational health centres, and is used during initial and renewal certification of medical fitness assessments. The second study focused on acquired visual function loss associated with ageing and retinal disease. Studies had suggested that visual acuity measurements were not a good predictor of early retinal disease where a significant loss in VA was perceptible only if the majority of the photoreceptors were dysfunctional (Sunness et al., 1997; Gellar et al., 1992). Coupled with the high inter-session measurement variablitiyfor visual acuity (Patel et al., 2008), the aim of the study was to identify the most appropriate and sensitive clinical test capable of detecting early morphological changes associated with early Age-related Macular Degeneration (AMD). A total of 71 subjects were recruited for the study, a normal control group (n = 45) and an active AMD group (n = 26) of varying disease severity. Subjects underwent extensive clinical screening to ensure they adhered to strict,inclusion criteria. Advanced psychophysical assessment of visual function for visual acuity, contrast sensitivity, mesopic and photopic temporal and chromatic sensitivity were conducted. Exclusive to this study was the refinement of a new temporal resolution test. Significant clinical predictors for early-AMD were determined based on bivariate regression analysis and entered stepwise into a multiple linear regression model, controlling for eye and age. Yellow-blue (YB) chromatic discrimination was identified as the best clinical predictor, explaining an additional 9 %-12 % of the variance for early-AMD detection and - 22 %-25 % for AMD disease. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis confirmed YB chromatic sensitivity to be 13.5 % more sensitive and 16.7 % more specific than the standard clinical Log MAR acuity test for detecting early-AMD retinal changes. A review of AMD and diabetic case studies also indicated that accurate Psychophysical methods of assessing chromatic sensitivity enhanced our clinical ability to detect, quantify and monitor changes associated with retinal disease and / or treatment outcomes.

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Howard, Newton. « Approach to study the brain : towards the early detection of neurodegenerative disease ». Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f81e9d4-ac91-444f-b966-ce1fc665b065.

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Neurodegeneration is a progressive loss of neuron function or structure, including death of neurons, and occurs at many different levels of neuronal circuitry. In this thesis I discuss Parkinson’s Disease (PD), the second most common neurodegenerative disease (NDD). PD is a devastating progressive NDD often with delayed diagnosis due to detection methods that depend on the appearance of visible motor symptoms. By the time cardinal symptoms manifest, 60 to 80 percent or more of the dopamine-producing cells in the substantia nigra are irreversibly lost. Although there is currently no cure, earlier detection would be highly beneficial to manage treatment and track disease progression. However, today’s clinical diagnosis methods are limited to subjective evaluations and observation. Onset, symptoms and progression significantly vary from patient to patient across stages and subtypes that exceed the scope of a standardized diagnosis. The goal of this thesis is to provide the basis of a more general approach to study the brain, investigating early detection method for NDD with focus on PD. It details the preliminary development, testing and validation of tools and methods to objectively quantify and extrapolate motor and non-motor features of PD from behavioral and cognitive output during everyday life. Measures of interest are categorized within three domains: the motor system, cognitive function, and brain activity. This thesis describes the initial development of non-intrusive tools and methods to obtain high-resolution movement and speech data from everyday life and feasibility analysis of facial feature extraction and EEG for future integration. I tested and validated a body sensor system and wavelet analysis to measure complex movements and object interaction in everyday living situations. The sensor system was also tested for differentiating between healthy and impaired movements. Engineering and design criteria of the sensor system were tested for usability during everyday life. Cognitive processing was quantified during everyday living tasks with varying loaded conditions to test methods for measuring cognitive function. Everyday speech was analyzed for motor and non-motor correlations related to the severity of the disease. A neural oscillation detection (NOD) algorithm was tested in pain patients and facial expression was analyzed to measure both motor and non-motor aspects of PD. Results showed that the wearable sensor system can measure complex movements during everyday living tasks and demonstrates sensitivity to detect physiological differences between patients and controls. Preliminary engineering design supports clothing integration and development of a smartphone sensor platform for everyday use. Early results from loaded conditions suggest that attentional processing is most affected by cognitive demands and could be developed as a method to detect cognitive decline. Analysis of speech symptoms demonstrates a need to collect higher resolution spontaneous speech from everyday living to measure speech motor and non-motor speech features such as language content. Facial expression classifiers and the NOD algorithm indicated feasibility for future integration with additional validation in PD patients. Thus this thesis describes the initial development of tools and methods towards a more general approach to detecting PD. Measuring speech and movement during everyday life could provide a link between motor and cognitive domains to characterize the earliest detectable features of PD. The approach represents a departure from the current state of detection methods that use single data entities (e.g.one-off imaging procedures), which cannot be easily integrated with other data streams, are time consuming and economically costly. The long-term vision is to develop a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease.

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Hartnick, Maria Diana. « Echocardiography for early detection of heart disease in high risk diabetic patients ». Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/1566.

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Masters of Technology: Radiography in the Faculty of Health and Wellness Sciences at the Cape Peninsula University of Technology 2015
Introduction: Diabetes mellitus is a chronic disease with a significant impact on personal lifestyle and wellbeing. It is associated with a high prevalence of myocardial disease, the early detection of which is important for prevention of disease progression. Although echocardiography is recognised as a leading cardiovascular imaging modality, there has been limited work on its role in the early detection of diabetes-related myocardial dysfunction. The aim of this study was therefore to evaluate the role of echocardiography in the early detection of diabetes-related myocardial disease, in a population with a high prevalence of type 2 diabetes mellitus. Methodology: A single sonographer, blinded to individual biochemical markers conducted detailed echocardiographic examinations on 407 participants from a Cape Town community with a high prevalence of diabetes mellitus. Participants were subsequently stratified by biochemical status, as normoglyceamia or hyperglycaemia. The echocardiographic features of the two groups were compared using the Pearson chi-squared and Mann-Whitney U tests. Findings: Hyperglycaemia was associated with left atrium (LA) enlargement (p ˂ 0.0014), aortic enlargement (p ˂ 0.0067) and inter-ventricular septal (IVS) thickening (p ˂ 0.0001). Conclusion: The findings suggest that echocardiography can be a useful screening tool for myocardial dysfunction in Type 2 diabetes mellitus.

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Ren, Jinyi [Verfasser], et Michael [Akademischer Betreuer] Ewers. « Automated detection of early-stage Alzheimer’s Disease / Jinyi Ren ; Betreuer : Michael Ewers ». München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1211957322/34.

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F, Miraglia. « Development of molecular biosensors for the detection of alpha-synuclein aggregation in cells ». Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096217.

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Introduction: Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, which affects more than 6 million people worldwide. PD is a progressive multiorgan proteinopathy, also called synucleinopathy, because of the abnormal neuronal accumulation of misfolded alpha-synuclein (αS), an intrinsically disorder protein (IDP) of 140 amino acids. αS is expressed ubiquitously in the cells and has been particularly found associated with membranes in the pre-synaptic terminal, suggesting a role in the regulation of vesicle trafficking, synaptic transmission and synaptic plasticity. Environmental toxins, genetic mutations, amplification of αS gene trigger αS misfolding and aggregation in Lewy Bodies (LBs) and Lewy Neurites (LNs), typical histopathological marker in PD. This event leads to a gradual loss of dopaminergic neurons in substantia nigra pars compacta (SNpc) and the spreading of LB pathology not only in neuronal networks of the central nervous system, but also in the autonomic and peripheral nervous systems. For these reasons, PD manifest with a broad range of motor (e.g. rigidity, tremor and bradykinesia) and non-motor symptoms (e.g. anosmia, constipation, cognitive dysfunction). However, the onset of motor symptoms occurs only when about the 50% of dopaminergic neurons are lost. Therefore, identifying the early step that trigger neuronal toxicity might help in halting neurodegeneration. Misfolding, oligomerization, and fibrillization of αS are thought to be central events in the onset and progression of PD. In fact, αS oligomers acts as seeds for the nucleation dependent process of αS intracellular aggregation, and as a template for the cell-to-cell transmission of αS pathology in a prion like manner. In light of this, we developed Alpha-synuclein FRET-based Biosensors (AFBs) capable to monitor any early αS conformational changes ubiquitously in the cell or in a specific subcellular compartment such as the ER, an early site of αS aggregation, under normal and stress conditions. Methods: AFBs were designed to detect different types of αS assembly such as: - relaxed vs globular αS conformation, where both CFP and YFP are cloned respectively at the C-terminal and N-terminal of the same molecule of WT human αS (intramolecular AFB); - parallel multimeric structures based on N-N and C-C termini interactions between molecules, where both fluorophores (CFP and YFP) are cloned separately at the C-terminal of WT human αS (intermolecular AFBs CC); - antiparallel multimers based on C-N termini interactions, where CFP and YFP are cloned separately and respectively at the C-terminal and N-terminal of WT human αS (intermolecular AFBs CN). AFBs were tested in two different cell lines: SH-SY5Y human neuroblastoma cell line and an inducible cell line stably expressing αS in the ER (iSH-SY5Y). Two different FRET methodologies were used to analyses AFBs behaviour: Sensitized Emission (SE) in SH-SY5Y living cells and Acceptor Photobleaching (AP) in iSH-SY5Y fixed cells. Results: interesting outcomes arose by the analysis of AFBs in iSH-SY5Y cells with AP. AFBs provided a radical different behaviour in the ability of detecting conformational differences in αS structure. Intramolecular AFB revealed a closed conformation of monomeric αS under normal conditions, whereas intermolecular AFBs highlighted a multimerization of αS in the ER, which occurred in an antiparallel orientation of αS monomers in normal condition and with a parallel orientation under the effect of leupeptin, an inhibitor of αS degradation pathway. Discussion and Conclusions: these findings suggested that AFBs represent a valuable strategy to detect conformational changes in αS structures in physiological condition and under the effect of stress stimuli. To the best of our knowledge they are the first fluorescent probes capable of detecting different type of αS multimers in the ER, an early site of pathological αS aggregation. Therefore, AFBs represent an interesting investigative approach on αS multimerization and, in the future, might be employed as a powerful tool for the screening of therapeutic agents which aim to halt αS pathological aggregation from the early phase and to prevent the irreversible progression of PD.

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Warton, Fleur L. « The effects of early developmental stress and exercise intervention on neurodegeneration in a rat model of Parkinson's disease ». Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/11069.

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Includes bibliographical references (p. 224-253).
Early developmental stress has been shown to produce numerous deleterious effects, e.g. the later development of affective disorders, and this has been related to chronic enhanced hypothalamic-pituitary-adrenal axis activity. Animal studies have shown that maternally separated rats exhibit increased anxiety- and depression-like behaviour in adulthood, although other evidence shows hyperactivity and impulsivity in such cases. Given that stress has these behavioural effects, it is of interest to determine whether early developmental stress might enhance the toxicity of a later unrelated neural insult. The 6-hydroxydopamine (6-OHDA) model of Parkinson's disease involves the selective unilateral lesion of nigrostriatal dopamine neurons. In this group of studies it was hypothesized that maternal separation might enhance the toxic effects of 6-OHDA.

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Jalloul, Nahed. « Development of a system of acquisition and movement analysis : application on Parkinson's disease ». Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S096/document.

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Le travail présenté dans ce mémoire porte sur le développement d'un système de surveillance ambulatoire pour la détection de la dyskinésie induite par la Levodopa (LID) chez les patients de la maladie de Parkinson (PD). Le système est composé d’unités de mesure inertielle (IMUs) qui recueillent des signaux de mouvement chez des sujets sains et des patients parkinsoniens. Des méthodes différentes sont évaluées pour la détection de LID avec et sans classification des activités. Les données recueillies auprès des sujets sains sont utilisées pour concevoir un classificateur d'activité fiable. Par la suite, un algorithme qui effectue la classification des activités et la détection de la dyskinésie sur les données recueillies auprès de des patients parkinsoniens est proposé. Une nouvelle approche basée sur l'analyse de réseau complexe est également explorée et présente des résultats intéressants. Les méthodes de traitement développées ont été intégrées dans une plateforme complète d’analyse nommée PARADYSE
The work presented in this thesis is concerned with the development of an ambulatory monitoring system for the detection of Levodopa Induced Dyskinesia (LID) in Parkinson’s disease (PD) patients. The system is composed of Inertial Measurement Units (IMUs) that collect movement signals from healthy individuals and PD patients. Different methods are evaluated which consist of LID detection with and without activity classification. Data collected from healthy individuals is used to design a reliable activity classifier. Following that, an algorithm that performs activity classification and dyskinesia detection on data collected from PD patients is tested. A new approach based on complex network analysis is also explored and presents interesting results. The evaluated analysis methods are incorporated into a platform PARADYSE in order to further advance the system’s capabilities

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Takač, Boris. « Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection ». Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/668652.

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Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.
Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificidad

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Ruffmann, Claudio. « Detection of alpha-synuclein conformational variants from gastro-intestinal biopsy tissue as a potential biomarker for Parkinson's disease ». Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:3cddebda-aaf4-40c5-b026-9365aa16fdd7.

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Gastrointestinal (GI) alpha-synuclein (ASN) detection may represent a clinically useful biomarker of Parkinson's disease (PD), but this has been challenged by conflicting results of recent studies employing different immunohistochemical (IHC) methods and reporting diverse morphological patterns with variable biological interpretation. To increase sensitivity and specificity, we applied three different techniques to detect different possible conformations of ASN in GI tissue derived from biopsies of the GI tract, which were obtained from a longitudinally followed, clinically well-characterized cohort of PD subjects and healthy controls (HC) (Oxford Discovery study). With IHC, we used antibodies reactive for total (T-ASN-Abs), phosphorylated (P-ASN-Abs) and oligomeric (O-ASN-Abs) ASN; with the ASN Proximity Ligation Assay (AS-PLA), we targeted oligomeric ASN species specifically; finally, with the Paraffin-Embedded Tissue Blot (PET-Blot) we aimed to detect fibrillary conformations of ASN specifically. Optimisation and validation of the PET-Blot and PLA techniques was carried out with studies on brain tissue from subjects with ASN pathology, and these experiments were used to gain insight into morphology and distribution of different conformational variants of ASN in the brain of subjects with Lewy pathology. We specified all the detected morphological staining patterns with each technique interpreting them as pathologic or non-specific. Correlation to clinical symptoms was assessed to investigate the potential predictive or diagnostic value of specific staining patterns as biomarkers. A total of 163 GI tissue blocks were collected from 51 PD patients (113 blocks) and 21 healthy controls (50 blocks). In 31 PD patients, GI biopsies had been taken before PD diagnosis (Prodromal PD group); while in 20 PD patients biopsies were obtained after PD diagnosis (Manifest PD group). The majority of these tissues blocks were from large intestine (62%), followed by small intestine (21%), stomach (10%) and oesophagus (7%). With IHC, four ASN staining patterns were detected in GI tissue (Neuritic, Ganglionic, Epithelial, and Cellular), while two distinct staining patterns were detected with AS-PLA (cellular and diffuse signal) and with AS-PET-Blot (ASN-localised and peri-crypt signal). The level of agreement between different techniques was generally low, and no single technique or staining pattern was able to reliably distinguish PD patients (Prodromal or Manifest) from HC. Overall, our study suggests that even specific detection of ASN conformational variants currently considered pathologic was not adequate for the prediction of PD. Future studies with these or other novel techniques focusing on the upper part of the GI tract could overcome current limitations in sensitivity and specificity.

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Timmaraju, Venkat Krishna Chaitanya. « Simulating the early detection and intervention of vascular disease in the caerphilly cohort ». Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/55711/.

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Introduction: The purpose of the project is to simulate the effect of hypothetical intervention on risk of vascular disease in the Caerphilly cohort. The cohort comprises a total population sample of 2959 men aged 45--59 years at the recruitment who has been followed up for 20 years. During that time there has been particular emphasis on assessing exposure to vascular risk factors and assessing vascular related outcomes. Aim: The aim of the thesis is to estimate the effects at population level of public health interventions to change the levels of modifiable risk factors for the vascular disease. Methods: Various statistical techniques such as logistic, fractional polynomial and Cox's proportional hazards models along with various parametric models were used to analyse the data. New risk prediction models were estimated and compared with the existing models in the literature. Various standard simulation techniques were used to simulate hypothetical data using Caerphilly data parameters. Hypothetical interventions were carried out on these generated samples to assess the public health impact. Results: Multivariate analysis suggested that the combined effect of psychological variables measured in the study were significantly associated with the increased risk of MI. New risk prediction models constructed using the Caerphilly study data showed that they were significantly different from the standard available models from the literature. Simulation results suggested that there could be a reduction MI events by 25--30% and stroke events by 50--55% using plausible intervention scenarios available from the literature review. Conclusion: A hypothetical intervention to modify psychological factors showed a higher reduction in MI events. Therefore, plausible interventions to modify psychological factors should be commissioned along with the standard biological and behavioural interventions.

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Hett, Kilian. « Multi-scale and multimodal imaging biomarkers for the early detection of Alzheimer’s disease ». Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0011/document.

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La maladie d’Alzheimer est la première cause de démence chez les personnes âgées. Cette maladie est caractérisée par un déclin irréversible des fonctions cognitives. Les patients atteints par la maladie d’Alzheimer ont de sévères pertes de mémoire et ont de grandes difficultés à apprendre de nouvelles informations ce qui pose de gros problèmes dans leur vie quotidienne. À ce jour, cette maladie est diagnostiquée après que d’importantes altérations des structures du cerveaux apparaissent. De plus, aucune thérapie existe permettant de faire reculer ou de stopper la maladie. Le développement de nouvelles méthodes permettant la détection précoce de cette maladie est ainsi nécessaire. En effet, une détection précoce permettrait une meilleure prise en charge des patients atteints de cette maladie ainsi qu’une accélération de la recherche thérapeutique. Nos travaux de recherche portent sur l’utilisation de l’imagerie médicale, avec notamment l’imagerie par résonance magnétique (IRM) qui a démontrée ces dernières années son potentiel pour améliorer la détection et la prédiction de la maladie d’Alzheimer. Afin d’exploiter pleinement ce type d’imagerie, de nombreuses méthodes ont été proposées récemment. Au cours de nos recherches, nous nous sommes intéressés à un type de méthode en particulier qui est basé sur la correspondance de patchs dans de grandes bibliothèques d’images. Nous avons étudié ces méthodes à diverses échelles anatomiques c’est à dire, cerveaux entier, hippocampe, sous-champs de l’hippocampe) avec diverses modalités d’IRM (par exemple, IRM anatomique et imagerie de diffusion). Nous avons amélioré les performances de détection dans les stades les plus précoces avec l’imagerie par diffusion. Nous avons aussi proposé un nouveau schéma de fusion pour combiner IRM anatomique et imagerie de diffusion. De plus, nous avons montré que la correspondance de patchs était améliorée par l’utilisation de filtres dérivatifs. Enfin, nous avons proposé une méthode par graphe permettant de combiner les informations de similarité inter-sujet avec les informations apportées par la variabilité intra-sujet. Les résultats des expériences menées dans cette thèse ont montrées une amélioration des performances de diagnostique et de prognostique de la maladie d’Alzheimer comparé aux méthodes de l’état de l’art
Alzheimer’s disease (AD) is the most common dementia leading to a neurodegenerative process and causing mental dysfunctions. According to the world health organization, the number of patients having AD will double in 20 years. Neuroimaging studies performed on AD patients revealed that structural brain alterations are advanced when the diagnosis is established. Indeed, the clinical symptoms of AD are preceded by brain changes. This stresses the need to develop new biomarkers to detect the first stages of the disease. The development of such biomarkers can make easier the design of clinical trials and therefore accelerate the development of new therapies. Over the past decades, the improvement of magnetic resonance imaging (MRI) has led to the development of new imaging biomarkers. Such biomarkers demonstrated their relevance for computer-aided diagnosis but have shown limited performances for AD prognosis. Recently, advanced biomarkers were proposed toimprove computer-aided prognosis. Among them, patch-based grading methods demonstrated competitive results to detect subtle modifications at the earliest stages of AD. Such methods have shown their ability to predict AD several years before the conversion to dementia. For these reasons, we have had a particular interest in patch-based grading methods. First, we studied patch-based grading methods for different anatomical scales (i.e., whole brain, hippocampus, and hippocampal subfields). We adapted patch-based grading method to different MRI modalities (i.e., anatomical MRI and diffusion-weighted MRI) and developed an adaptive fusion scheme. Then, we showed that patch comparisons are improved with the use of multi-directional derivative features. Finally, we proposed a new method based on a graph modeling that enables to combine information from inter-subjects’ similarities and intra-subjects’ variability. The conducted experiments demonstrate that our proposed method enable an improvement of AD detection and prediction

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Valluru, Keerthi Srivastav. « Study of Biomolecular Optical Signatures for Early Disease Detection and Cell Physiology Monitoring ». University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1213627946.

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Li, Jiangwei. « Applications of single-molecule detection in early disease diagnosis and enzymatic reaction study ». [Ames, Iowa : Iowa State University], 2008.

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Pu, Christopher Hao. « Quantitative mass spectrometry to discover interactors of parkin E3 ubiquitin ligase, a protein implicated in early-onset Parkinson's disease ». Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/17757.

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Ubiquitylation is a major post-translational modification based on a network of about six hundred E3 ubiquitin ligases in human. It is involved in several processes such as proteolysis, vesicle trafficking and DNA damage response. Mutations in PARK2, which encode parkin E3 ubiquitin ligase, account for half of autosomal recessive juvenile Parkinsonism cases, an early onset form of Parkinson’s disease. Multiple PARK2 mutations underlie the RING domain, which contains ligase activity. This finding suggests an inability for substrate ubiquitylation may trigger neurodegeneration. We used a quantitative proteomics approach to seek identifying parkin substrates and interactors. We first developed and tested new methods to enrich for ubiquitylated proteins that could potentially be used to study the influence of parkin on the ubiquitin proteome. In our first approach, ubiquitin conjugates were purified from SH-SY5Y neuroblastoma expressing His8-biotin-ubiquitin by tandem affinity purification. A second approach to purify ubiquitylated proteins was based on affinity chromatography using S5a proteasome receptor that bound to poly-ubiquitylated proteins. We determined that both approaches were not adequate for identifying low abundance parkin substrates. We then sought to identify which proteins were associated with parkin. Parkin interactors were enriched from SH-SY5Y expressing FLAG-parkin versus endogenous parkin by anti-FLAG immunoprecipitation in the context of SILAC. Proteins from the neuroendocrine chromogranin-secretogranin family were highly enriched suggesting a potential granin vesicle trafficking role for parkin. CCCP, a mitochondrial uncoupling agent was also employed to investigate parkin ligase interactors during mitochondrial stress since parkin localizes to mitochondria to promote mitophagy upon a reduction in mitochondrial membrane potential. Several actin related proteins were enriched from FLAG-parkin cells treated with CCCP including non-muscle unconventional signaling myosin suggesting a potential role for these proteins during parkin-mediated mitophagy.

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Hu, Kun. « Fine-grained Human Action Recognition for Freezing of Gait Detection ». Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27286.

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Freezing of gait (FoG) presents as a sudden and brief episode of movement cessation despite the intention to continue walking. As a common symptom of Parkinson's Disease, early detection and quantification of FoG are of great importance in clinical practice. Therefore, this thesis focuses on vision-based and pressure-based FoG detection methods, which were seldom investigated in the past. The task can be treated as a human action recognition problem. Although various deep architectures have achieved encouraging performance for general action recognition, FoG events contain fine-grained patterns, which requires novel deep architectures to learn the domain knowledge. Hence, fine-grained deep architectures are studied, and the major contributions of this thesis are as follows: 1. A graph-based neural network is proposed for vision-based FoG detection by representing a temporal video segment as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy is studied to eliminate the resource expensive annotations. 2. A graph sequence recurrent neural network is proposed to formulate long-term graph temporal patterns, which takes graph sequences of dynamic structures as inputs and characterizes FoG patterns by graph recurrent cells. 3. An adversarial spatio-temporal network is proposed to learn FoG patterns across multiple levels using footstep pressure sequences. The adversarial training scheme aims to obtain subject-independent representations to alleviate the issue of high inter-subject variance. 4. A graph fusion neural network is introduced for multimodal learning using footstep pressure maps, video recordings and their associated optical flows. Multimodal graphs are introduced by treating the encoded features of each modality as vertex-level inputs. Comprehensive experiments demonstrate the effectiveness of the proposed methods.

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Sjöberg, Rebecca. « Astrocyte-specific druggable protein as PET-ligand target for early detection of Alzheimer's disease ». Thesis, KTH, Skolan för bioteknologi (BIO), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215308.

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PASQUINI, JACOPO. « Multimodal Magnetic Resonance Imaging for the identification of early Multiple System Atrophy biomarkers ». Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/890787.

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Background. Multiple system atrophy (MSA) is a rare, sporadic disease characterized by autonomic failure and a various combination of parkinsonism and cerebellar dysfunction. Currently, development of new treatment strategies in MSA is hampered by the lack of reliable diagnostic and disease-progression biomarkers. The aim of this study was to investigate brain microstructural abnormalities in MSA through diffusion and neuromelanin-sensitive magnetic resonance imaging (MRI) and their relationship with clinical manifestations. Methods. Clinical evaluation and MRI were performed on 11 MSA patients, 19 Parkinson’s Disease (PD) and 18 healthy controls (HC). MRI scans included structural, diffusion (dMRI) and neuromelanin-sensitive sequences. dMRI was applied through a novel technique, neurite orientation dispersion and density imaging (NODDI). Compared to previous dMRI techniques, NODDI allows the simultaneous evaluation of the integrity of the intracellular and extracellular compartments, while gathering information on the orientation of axons and dendrites. Neuromelanin-sensitive MRI was used to quantitively investigate the integrity of substantia nigra (SN) and locus coeruleus (LC). Results. Median duration of symptoms in MSA patients was 3 years (range 1-6). Age was not significantly different across subgroups. Compared to PD, MSA patients had reduced neurite density index (NDI) in the middle cerebellar peduncle (MCP) and in the pons (Mann-Whitney U=44.0, p=0.019 and U=52.0, p=0.050), indicating white matter degeneration in these locations, and increased free water fraction (FWF), indicating grey matter loss, in the putamen, caudate and cerebellar lobule VI grey matter (U=146.0, p=0.019; U=145.0, p=0.021; U=154.0, p=0.006 respectively). Neuromelanin content was not different in SN and LC between PD and MSA, although this was reduced in the posterior SN and intermediate part of LC compared to HCs (Kruskal Wallis H=11.363, p=0.003 and H=13.788, p=0.001), indicating similar, significant degeneration of these nuclei in both conditions. No significant correlations were found between motor scores and MRI parameters in the SN, putamen, and MCP and pons. LC neuromelanin loss in the rostral and/or intermediate sections was significantly associated with greater cognitive, depressive and REM sleep behaviour disorder (RBD) symptoms scores in MSA. Symptoms of dysautonomia were not associated with diffusion or neuromelanin content measures. Conclusion. Multimodal MRI with diffusion and neuromelanin evaluation may help define structural abnormalities in the early stages of MSA. NODDI seems a promising technique to simultaneously evaluate multiple microstructural parameters in critical locations of MSA pathology, such as the basal ganglia, cerebellum, and pons. Neuromelanin content evaluation is useful for defining SN and LC degeneration, although this occurs similarly in MSA and PD. In MSA, LC degeneration is associated with greater depressive, cognitive and RBD symptoms. Longitudinal investigations are needed to establish whether these MRI parameters may serve as disease-progression biomarkers.

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Umemura, Atsushi. « Diagnostic Accuracy of Apparent Diffusion Coefficient and 123I-Metaiodobenzylguanidine for Differentiation of Multiple System Atrophy and Parkinson's Disease ». Kyoto University, 2015. http://hdl.handle.net/2433/200316.

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Kaladytė, Lokominienė Rūta. « Cognitive functions in early-stage Parkinson's disease according to computerised test results, their relationship with biological markers and clinical non-cognitive symptoms ». Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140303_135449-49961.

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The aim of the study: to evaluate the cognitive functions of patients with early-stage Parkinson‘s disease (PD) without dementia using Cambridge Neuropsychological Test Automated Battery (CANTAB Eclipse 3.0.0), to compare the obtained results with the data of control subjects, to determine the associations of the variables of cognitive testing with biological markers and clinical non-cognitive symptoms of PD. Objectives: to examine attention, memory, visuospatial and executive functions of patients with early-stage PD without dementia using CANTAB Eclipse 3.0.0 and to compare the obtained results with the data of control subjects; to investigate the relationship between the cognitive functions of patients with early-stage PD and the severity of disease measured by UPDRS score, the echogenic properties of brainstem nigral substance (SN) examined by transcranial sonography (TCS), the striatal binding of presinaptic dopamine transporter determined by single-photon emission computed tomography (SPECT) with I¹²³-FP-CIT, sleep, fatigue, demographic factors and quality of life scores, the usage of medications for early-stage PD; to analize the diagnostic characteristics of particular computerised tests for evaluation of cognitive function in patients with early-stage PD. Methods. The study was performed at the Department of Neurology of Vilnius University Hospital Santariškių Klinikos. 115 patients diagnosed with clinically probable early-stage PD who met inclusion criteria and... [to full text]
Darbo tikslas: įvertinti ankstyva Parkinsono liga (PL) be demencijos sergančių asmenų pažinimo funkcijas naudojant Kembridžo kompiuterinės neuropsichologinio ištyrimo sistemos testų rinkinį, palyginti rezultatus su kontrolinės grupės asmenų duomenimis bei nustatyti kognityvinių rodiklių ryšius su biologiniais žymenimis ir klinikiniais nekognityviniais PL simptomais. Darbo uždaviniai: ištirti ankstyva PL sergančių asmenų dėmesio, atminties, regos erdvinę ir vykdomąsias funkcijas, naudojant kompiuterizuotų testų rinkinį CANTAB eclipse 3.0.0, ir palyginti juos su kontrolinių asmenų duomenimis; nustatyti pacientų kognityvinių funkcijų ryšį su UPLVS skale įvertintu ligos sunkumu, transkranijinės sonografijos (TKS) metodu nustatytu juodosios medžiagos (JM) echogeniškumu, presinapsinio dopamine transporterio koncentracija dryžuotame kūne, nustatyta radionuklidinės kompiuterinės tomografijos (RKT) su I¹²³-FP-CIT būdu, miego, nuovargio bei demografiniais veiksniais, gyvenimo kokybės rodikliais, PL gydyti skiriamų vaistų vartojimu; išanalizuoti kompiuterizuotais testais įvertintų kognityvinių funkcijų diagnostinę vertę sergant ankstyva PL. Metodai. Tyrimas atliktas Vilniaus universiteto ligoninės Santariškių klinikų Neurologijos centre. Atrinkta 115 pacientų, sergančių ankstyva kliniškai tikėtina PL, kurie atitiko įtraukimo kriterijus bei nebuvo neįtraukimo kriterijų, ir 42 pagal amžių, lytį, mokymosi trukmę atrinkti kontroliniai tiriamieji, kurie nesirgo PL ar kitomis... [toliau žr. visą tekstą]

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