Les infections virales des voies respiratoires (IVVRs) causées par certains virus comme la grippe et le COVID-19 ont un impact significatif sur la santé publique et l’économie mondiale. Ces infections touchent un nombre important de personnes dans le monde et exercent une pression immense sur les systèmes de santé. Pour atténuer les effets néfastes des IVVRs, il est important de développer des techniques de détection précoce capables d’identifier les personnes infectées même si elles ne présentent aucun symptôme. Une telle détection permet un isolement et raitement rapide, ce qui réduit le risque de transmission et permet des interventions de santé publique ciblées pour limiter la propagation de l’infection. Les méthodes de détection actuelles telles que la réaction en chaîne par polymérase (RCP) démontrent une sensibilité et une spécificité élevées, atteignant des taux de détection de 100% avec certaines méthodes de test disponibles dans le marché. De plus, les approches actuelles d’apprentissage automatique pour la détection des IVVRs, montrent des résultats prometteurs ; cependant, les méthodes actuelles reposent souvent sur l’apparition des symptômes, exigent un équipement coûteux et un personnel formé, et fournissent des résultats relativement retardés. Notre projet vise à étudier la faisabilité de l’utilisation d’un algorithme d’apprentissage automatique entraîné sur des données physiologiques provenant de biocapteurs portables lors d’un protocole de test de marche sur escalier pour prédire le niveau d’inflammation associé aux IVVRs. De plus, l’étude vise à identifier les indicateurs les plus prédictifs des IVVRs. Des participants en bonne santé ont été recrutés et inoculés avec un vaccin antigrippal vivant pour induire une réponse immunitaire. Au cours d’une série de tests d’escalier contrôlés cliniquement, des physiomarqueurs tels que la fréquence respiratoire et la fréquence cardiaque ont été meusurés à l’aide de biocapteurs portables. Les données collectées ont été utilisées pour développer un modèle de prédiction en ayant recours aux algorithmes d’apprentissage automatique, combinés avec un réglage d’hyperparamètres et en écartant un participant à la fois lors de l’entraînement du modèle. L’étude a développé avec succès un modèle prédictif qui démontre des résultats prometteurs dans la prédiction du niveau d’inflammation lié au vaccin induit. Notamment, les caractéristiques de variabilité de la fréquence cardiaque (VFC) dérivées du biocapteur portable présentaient le potentiel le plus élevé pour détecter le niveau d’inflammation, atteignant une sensibilité de 70% et une spécificité de 77%. Les implications du modèle de prédiction développé sont importantes pour les cliniciens et le grand public, notamment en termes d’autosurveillance et d’intervention précoce. Grâce aux algorithmes d’apprentissage automatique et des physiomarqueurs utilisés, en particulier les caractéristiques de VFC, cette approche a le potentiel de faciliter l’administration en temps opportun des traitements appropriés, atténuant ainsi l’impact des futures épidémies des IVVRs. L’intégration de biocapteurs portables et d’algorithmes d’apprentissage automatique fournit une stratégie innovante et efficace de détection précoce, permettant une intervention rapide et réduisant la charge sur les systèmes de santé / Viral respiratory tract infections (VRTIs) caused by certain viruses like influenza and COVID-19, significantly impact public health and the global economy. These infections affect a large number of people worldwide and put immense pressure on healthcare systems. To mitigate the detrimental effects of VRTIs, it is crucial to urgently develop accurate early detection techniques that can identify infected individuals even if they do not exhibit any symptoms. Timely detection allows for prompt isolation and treatment, reducing the risk of transmission and enabling targeted public health interventions to limit the spread of the infection. Current detection methods like polymerase chain reaction (PCR) demonstrate high sensitivity and specificity, reaching 100% detection rates with some commercially available testing methods. Additionally, current machine learning approaches for automatic detection show promising results; however, current methods often rely on symptom onset, demand expensive equipment and trained personnel, and provide delayed results. This study aims to investigate the feasibility of utilizing a machine learning algorithm trained on physiological data from wearable biosensors during a stair stepping task protocol to predict the level of inflammation associated with VRTIs. Additionally, the study aims to identify the most predictive indicators of VRTIs. Healthy participants were recruited and inoculated with a live influenza vaccine to induce an immune response. During a series of clinically controlled stair tests, physiomarkers such as breathing rate and heart rate were monitored using wearable biosensors. The collected data were employed to develop a prediction model through the utilization of gradient boosting machine learning algorithms, which were combined with hyperparameter tuning and a leave-one-subject-out approach for training. The study successfully developed a predictive model that demonstrates promising results in predicting the level of inflammation related to the induced VRTI. Notably, heart rate variability (HRV) features derived from the wearable biosensor exhibited the highest potential in detecting the level of inflammation, achieving a sensitivity of 70% and a specificity of 77%. The implications of the developed prediction model are significant for clinicians and the general public, particularly in terms of self-monitoring and early intervention. By leveraging machine learning algorithms and physiomarkers, specifically HRV features, this approach holds the potential to facilitate the timely administration of appropriate treatments, thereby mitigating the impact of future VRTI outbreaks. The integration of wearable biosensors and machine learning algorithms provides an innovative and effective strategy for early detection, enabling prompt intervention and reducing the burden on healthcare system

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.

This thesis addresses the three principal questions concerning the development of CT urography for investigating haematuria and each question is the subject of a separate chapter. The questions are: What is the reasoning behind using CT urography? What is the optimum diagnostic strategy using CT urography? What are the problems with using CT urography and how may solutions be provided? Haematuria can signify serious disease such as urinary tract stones, renal cell cancer, upper tract urothelial cancer (UTUC) and bladder cancer (BCa). CT urography is defined as contrast enhanced CT examination of kidneys, ureters and bladder. The technique used here includes unenhanced, nephrographic and excretory-phases for optimized diagnosis of stones, renal masses and urothelial cancer respectively. The reasoning behind using excretory-phase CT urography for investigating haematuria is based on results showing its high diagnostic accuracy for UTUC and BCa. Patients with haematuria are classified as low risk or high risk for UTUC and BCa, by a risk score, determined by the presence/absence of risk factors: age > 50 years, visible or nonvisible haematuria, history of smoking and occupational exposure. The optimum diagnostic strategy for patients at high risk for urothelial cancer, uses CT urography as a replacement test for ultrasonography and intravenous urography and as a triage test for flexible and rigid cystoscopy, resulting in earlier diagnosis and potentially improving prognosis. For patients at low risk, ultrasonography, unenhanced and nephrographic-phase CT urography are proposed as initial imaging tests. Problems with using CT urography include false positive results for UTUC, which are eliminated by retrograde ureteropyelography-guided biopsy, an innovative technique, for histopathological confirmation of diagnosis. Recommendations for the NHS and possible future developments are discussed. CT urography, including excretory-phase imaging, is recommended as the initial diagnostic imaging test before cystoscopy for patients with haematuria at high risk for urothelial cancer.