Au Canada, le cancer du poumon est la cause principale de décès relié au cancer. À l’imagerie médicale, le cancer du poumon peut prendre la forme d’un nodule pulmonaire. La prise en charge menant au diagnostic définitif d’un nodule pulmonaire peut s’avérer complexe. La recherche en oncoprotéomique a permis le développement de nouveaux tests diagnostiques non-invasifs en cancer du poumon. Ceux-ci ont pour objectif d’évaluer le risque de malignité d’un nodule pour guider la prise en charge menant au diagnostic. Toutefois, l’impact économique de tels tests demeure inconnu. L’objectif de ce projet était de mesurer, en milieu de pratique réelle, l’utilisation des ressources en soins de santé pour l’investigation de nodules pulmonaires puis, de développer un modèle générique permettant d’évaluer l’impact économique au Québec des nouveaux tests protéomiques pour l’investigation de ces nodules. Tout d’abord, une revue de dossiers patients a été effectuée dans trois centres hospitaliers du Québec afin de mesurer les ressources en soins de santé et les coûts associés à l’investigation de nodules pulmonaires entre 0,8 et 3,0 cm. Par la suite, une analyse de minimisation de coûts a été effectuée à partir d’un modèle générique développé dans le cadre de ce projet. Ce modèle visait à comparer l’approche courante d’investigation à celle intégrant un test protéomique fictif afin de déterminer l’approche la moins dispendieuse. La revue de dossiers patients a permis de déterminer qu’au Québec, le coût moyen d’investigation d’un nodule pulmonaire est de 7 354$. Selon les résultats de l’analyse, si le coût du test protéomique est fixé en-deçà de 3 228,70$, l’approche intégrant celui-ci serait moins dispendieuse que l’approche courante. La présente analyse suggère que l’utilisation d’un test diagnostique protéomique non-invasif en début d’investigation pour un nodule de 0,8 à 3,0 cm, permettrait d’engendrer des économies pour le système de santé au Québec. / In Canada, lung cancer is the leading cause of death among cancer patients. Imaging technologies, such as computed tomography, allows the detection of potential lung cancers in the form of pulmonary nodules. The clinical pathway leading to the definitive diagnostic of a pulmonary nodule can be complex. Research in oncoproteomics has led to the development of novel noninvasive diagnostic tests in lung cancer. These tests aim to evaluate the risk of malignancy of a nodule in order to guide the clinical pathway leading to a diagnostic. However, the economic impact of such tests remains unknown. The objective of this project was to measure, in a real-life setting, health care resource utilization for the investigation of pulmonary nodules and then, develop a generic model to assess the economic impact in the province of Quebec of new proteomic tests for the investigation of these nodules. Firstly, a medical chart review was performed in three hospitals in Quebec to measure health care resource utilization for the investigation of pulmonary nodules of 0,8 to 3,0 cm. Then, a cost minimization analysis was performed by using a generic model developed for this project. This model compared the usual care to the approach integrating a fictive proteomic test in order to identify the less expensive approach. As per the medical chart review, the average cost for the investigation of a pulmonary nodule was $7,354. According to the results of the analysis, if the cost of the test is below $3,228.70, the approach integrating a proteomic test would be less expensive then the current approach. This study tends to demonstrate that the use of a noninvasive proteomic diagnostic test at the beginning of the investigation of a pulmonary nodule from 0,8 to 3,0 cm could generate savings for the health care system in Quebec.

Du point de vue de la médecine et des sciences du vivant, la médecine personnalisée (MP) est trop souvent réduite à cette idée d'adapter un diagnostic, une prédisposition ou un traitement, en fonction des caractéristiques génétiques d'un individu. Cependant, du point de vue des sciences humaines et sociales, la MP peut être considérée comme un phénomène social complexe en raison d'une existence propre et d'une composition sui generis, de l'effet de contraintes qu'il exerce sur les individus, d'un grand nombre d'interactions et d'interférences entre un grand nombre d'unités, mues d'incertitudes, d'indéterminations, de hasard, d'ordre et de désordre. Selon nous, cet autre point de vue permet de mieux étudier la MP par un travail de recherche en bioéthique, mais avec un nouvel objectif, opposé mais complémentaire de celui du droit et de la philosophie morale, et une nouvelle méthode. En effet, l'objectif de la bioéthique devrait être un travail de recherche prospectif questionnant les normes établies faisant face à un phénomène social complexe émergeant, non l'inverse. Ceci permet de déterminer les bénéfices pour la société, et ses individus, à laisser le phénomène émerger en son sein, et d'étudier des solutions possibles et probables et non des certitudes, pour le présent et le futur. De cette façon, les bénéfices identifiés pourront se produire. Mais cet objectif nécessite une méthode permettant d'étudier le fonctionnement du phénomène dans son ensemble, à l'échelle de la société, sans le réduire à l'a priori de certains individus, en privilégiant ses interactions à ses éléments : il s'agit de la modélisation théorique systémique inductive qualitative. L'idée clé est d'être dans une logique de découverte, non de preuve. Cette nouvelle approche nous a tout d'abord permis de comprendre que la MP ne devrait plus être nommée «personnalisée », ni même « génomique » ou de « précision», mais «médecine des données» (MD) étant donné le caractère centrale de la « donnée » (data) pour son fonctionnement. En effet, les finalités du phénomène semblent être, à partir d'une masse importante de données (génétiques), déduire (Datamining) ou induire (Big Data) différentes informations valorisables au niveau du soin, de la recherche et de l'industrie. Le moyen pour ça semble être le développement d'un réseau d'échange ou de partage d'échantillons biologiques, de données génétiques et d'informations entre patients, cliniciens, chercheurs et industriels, grâce à des voies de communication dématérialisées, qui centralisent le stockage des échantillons biologiques et des données génétiques, et une partie du traitement et de l'analyse, au niveau de centres de soin et de recherche académiques (France), et/ou d'entreprises privées (États-Unis), avec ou sans l'intermédiaire du clinicien. Les enjeux éthiques majeurs semblent donc résider dans les moyens et les modalités d'accès, de stockage et d'usage des données génétiques, car delà découle pour une organisation globalement similaire du phénomène un fonctionnement radicalement (social/libéral) opposé qui questionne certaines normes morales et juridiques. Au final, notre méthode nous a permis d'apporter différents arguments en faveur du consentement éclairé exprès électronique (e-CE) dynamique comme solution et moyen permettant un développement de la MD plus optimal concernant l'accès, le stockage et l'usage des données génétiques que ce soit pour le partage (France) ou l'échange (États-Unis) des données génétiques. / In the context of medicine and life sciences, personalized medicine (PM) is all too often reduced to the idea of adapting a diagnosis, predisposition or treatment according to the genetic characteristics of an individual. However, in human and social sciences, PM may be considered as a complex social phenomenon, due to the proper existence and unique composition of the constraints it imposes on individuals, the large number of interactions and interferences between a large number of units, rich in uncertainties, indeterminations, chance, order and disorder. We feel that this alternative point of view makes it possible to study PM more effectively by bioethics research approaches, but with a new objective, contrasting but complementary to those of law and moral philosophy, and a new method. Indeed, the objective of bioethics should be prospective studies questioning established norms in the face of emerging complex social phenomena, rather than the other way round. This makes it possible to determine the benefits, to society and its individuals, of allowing the phenomenon to emerge fully, and to study possible and probable solutions, rather than certainties, for the present and the future. This may allow the identified benefits to occur. However, this objective requires a method for studying the functioning of the phenomenon as a whole, at the scale of society, without a priori restriction to certain individuals, thereby favoring its interactions over its elements. Qualitative inductive systemic theoretical modeling is just such an approach. The key idea here is a rationale of discovery, rather than of proof. This new approach allowed us to understand that PM should not be called "personalized", or even "genomic" or "precision" medicine, and that the term "data medicine" (DM) should be favored, given the key role of data in its functioning. Indeed, the goal of this phenomenon seems to be to use a large mass of data (genetics) to deduce (data mining) or induce (big data) different types of information useful for medical care, research and industry. The means of achieving this end seems to be the development of a network for exchanging or sharing biological samples, genetic data and information between patients, clinicians, researchers and industrial partners, through electronic communication, with the central storage of biological samples and genetic data, and with treatment and analysis carried out at academic care and research centers (France) or in private companies (United States), with or without the involvement of a clinician. The major ethical issues thus seem to relate to the means and mode of access to, and the storage and use of genetic data, which may lead to a radically opposed (social/liberal) organizations and functioning, calling into question certain moral and legal standards. Finally, our method provided several arguments in favor of the use of dynamic electronic informed consent (e-CE) as a solution optimizing the development of PM in terms of genetic data access, storage and use, for the sharing (France) or exchange (United States) of genetic data.

Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.

Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements and compares a variety of models with the primary focus of Machine Learning and Deep Learning for the task of music recommendation and do so successfully by representing the task of recommendation as a multi-class extreme classification task with 100 000 distinct labels. By comparing fourteen different experiments, all implemented models successfully learn features such as time, location, user features and previous listening history in order to create context-aware personalized music predictions, and solves the cold start problem by using user demographic information, where the best model being capable of capturing the intended label in its top 100 list of recommended items for more than 1/3 of the unseen data in an offine evaluation, when evaluating on randomly selected examples from the unseen following week. / Informationsfiltrering och rekommendationssystem har använts och implementeratspå flera olika sätt från olika enheter sedan gryningen avInternet, och moderna tillvägagångssätt beror påMaskininlärrning samtDjupinlärningför att kunna skapa precisa och personliga rekommendationerför användare i en given kontext. Dessa modeller kräver data i storamängder med en varians av kännetecken såsom tid, plats och användardataför att kunna hitta korrelationer samt mönster som klassiska modellersåsom matris faktorisering samt samverkande filtrering inte kan. Dettaexamensarbete forskar, implementerar och jämför en mängd av modellermed fokus påMaskininlärning samt Djupinlärning för musikrekommendationoch gör det med succé genom att representera rekommendationsproblemetsom ett extremt multi-klass klassifikationsproblem med 100000 unika klasser att välja utav. Genom att jämföra fjorton olika experiment,så lär alla modeller sig kännetäcken såsomtid, plats, användarkänneteckenoch lyssningshistorik för att kunna skapa kontextberoendepersonaliserade musikprediktioner, och löser kallstartsproblemet genomanvändning av användares demografiska kännetäcken, där den bästa modellenklarar av att fånga målklassen i sin rekommendationslista medlängd 100 för mer än 1/3 av det osedda datat under en offline evaluering,när slumpmässigt valda exempel från den osedda kommande veckanevalueras.

Trotz Fortschritte in den letzten Dekaden ist das Langzeitüberleben von Nierentransplantaten unzureichend. Die Personalisierung der Behandlung kann dabei zu erheblichen Verbesserungen führen. Vor diesem Hintergrund wurde eine Kohorte von 587 Patienten im ersten Jahr nach der Transplantation untersucht und ein breites Spektrum von Markern zur langfristigen Prognose etabliert. In dieser Dissertation beschreibe ich in vier Manuskripten und zwei Kapiteln meine Arbeit zur personalisierten Transplantationsmedizin. Der klinische Verlauf von Patienten nach Nierentransplantation wurde untersucht. Die wichtigen Komplikationen standen im Vordergrund: Virusreaktivierungen – insbesondere die BK- und Cytomegalieviren – und akute Abstoßung. Folgende Analysen wurden durchgeführt: (i) Systematische Analyse der Assoziationen zwischen Virusreaktivierungen und deren Einfluss auf das Transplantationsergebnis; (ii) Bewertung der Auswirkungen antiviraler Behandlungsstrategien auf die Transplantationsergebnisse; (iii) Entwicklung eines Tools zur Prätransplantations-Risikoeinschätzung der Abstoßung und (iv) Erstellung eines mathematischen Modells für die personalisierte Charakterisierung der Immunantwort gegen das BK-Virus. Zusammengenommen haben die vier Studien das Potenzial, (i) die Patientenversorgung zu verbessern, (ii) die Überwachung von Virusreaktivierungen zu optimieren, (iii) Präventionsstrategien gegen virale Reaktivierungen zu stratifizieren, (iv) die Behandlung der Patienten an das individuelle Risiko akuter Abstoßung anzupassen, und (v) zur Personalisierung der Immuntherapie beizutragen. Die Studien zeigen, wie das große Datenvolumen einer klinischen Studie zur Weiterentwicklung der personalisierten Medizin unter Einsatz effektiver Strategien für Datenmanagement, Analyse und Interpretation genutzt werden kann. Es ist zu erwarten, dass diese Ergebnisse die klinische Praxis beeinflussen und so das langfristige Überleben und die Lebensqualität der Patienten verbessern. / In spite of the developments in the last decades, long-term graft survival rates in kidney transplantation are still poor: Personalization of treatment can thereby lead to a drastic improvement in long-term outcomes. With this goal, a cohort of 587 patients was characterized for a wide range of markers during the first post-transplantation year to assess their long-term prognosis. Here, I describe along four manuscripts and two chapters my work on personalized medicine for renal transplantation. In detail, we have studied the clinical evolution of patients with emphasis on two most relevant complications: viral reactivations – particularly those of BK virus and cytomegalovirus – and acute rejection. We have analysed in depth these phenomena by (i) exhaustively analysing the associations between different viral reactivations and their influence on transplantation outcome, (ii) evaluating the effects of antiviral treatment strategies on viral reactivation and other transplantation outcomes with emphasis on sex-associated differences, (iii) developing a tool for the pre-transplantation risk assessment of acute cellular rejection, and (iv) creating a mathematical model for the personalized characterization of the immune response against the BK virus under immunosuppression. Taken together, these studies have the potential of improving patient care, optimizing monitoring of viral reactivations, stratifying antiviral prevention strategies, tailoring immunosuppression and monitoring to the individual risk of acute rejection, and contributing to personalization of immunotherapy. They demonstrate how the large volume of data obtained within a clinical study can be employed to further the development of personalized medicine, employing effective data management, analysis and interpretation strategies. We expect these results to eventually inform clinical practice, thereby improving long-term survival and quality of life after kidney transplantation.

Au Québec, les personnes ayant une ascendance géographique des régions du Saguenay-Lac- Saint-Jean, de Charlevoix et de la Haute-Côte-Nord ont une prévalence plus élevée que le reste de la population québécoise d’être porteurs de certaines maladies héréditaires récessives. Depuis 2018, une offre de tests de porteurs en ligne est proposée par le Ministère de la Santé et des Services Sociaux du Québec pour quatre maladies autosomiques récessives : l’acidose lactique congénitale, la tyrosinémie héréditaire de type 1, la neuropathie sensitivomotrice avec ou sans agénésie du corps calleux et l’ataxie récessive spastique de Charlevoix-Saguenay. Ce même dépistage peut être offert en contexte clinique, chez des adultes éligibles lors de consultations en lien avec un désir de grossesse ou une grossesse en cours. Les objectifs de ce projet de recherche sont (1) de décrire l’expérience des patients ayant eu accès au dépistage de porteurs en contexte clinique et (2) d’identifier, analyser et comparer les enjeux éthiques soulevés par un dépistage de porteurs dans le cadre d’un programme structuré versus un dépistage de porteurs en contexte clinique. Pour ce faire, une série de questionnaires destinée aux patients auxquels le dépistage a été offert lors d’un rendez-vous en clinique a été mise en place et une analyse éthique à l’aide d’un cadre éthique de santé publique a été réalisée. À la lumière de ce projet, l’autonomie décisionnelle du patient est mise de l’avant. Des pistes de réflexion ainsi que des recommandations ont été développées afin de répondre au mieux aux besoins des personnes qui considèrent avoir recours à des tests de porteurs. / In Quebec, people with geographical ancestry from the Saguenay-Lac-Saint-Jean, Charlevoix and Haute-Côte-Nord regions have a higher prevalence than the rest of the Quebec population of being carriers of specific recessive hereditary diseases. Since 2018, online carrier testing has been offered by the Ministère de la Santé et des Services Sociaux du Québec for four autosomal recessive diseases: congenital lactic acidosis, hereditary tyrosinemia type 1, sensitivomotor neuropathy with or without agenesis of the corpus callosum and Charlevoix-Saguenay recessive spastic ataxia. This same screening can be offered in a clinical setting, to eligible adults during consultations related to a pregnancy desire or a pregnancy in progress. The objectives of this research project are (1) to describe the experience of patients who have had access to carrier screening in a clinical setting and (2) to identify, analyze and compare the ethical issues raised by carrier screening in a structured program versus carrier screening in a clinical setting. To this end, a series of questionnaires was administered to patients who were offered screening during a clinic appointment, and an ethical analysis was carried out using a public health ethics framework. In the light of this project, the patient's decision-making autonomy is emphasized. A number of ideas and recommendations have been developed to best meet the needs of people considering carrier testing.

No description available.

Focusing on personalized grammar tasks, this study dives into the integration of Generative Artificial Intelligence into English as a Second Language education. By utilizing a mixed methods approach, incorporating both qualitative and quantitative analyses the study explores how personalized learning can be improved by employing ChatGPT. Results from the study indicate that GAI-driven personalization significantly enhances student engagement and motivation. This offers a promising path for tailoring education to individual learner needs toward a more inclusive classroom. A central outcome of this study is the proposal of a new theoretical framework the Personalization-Motivation Integration Framework (PMIF). This framework clarifies the synergistic effects of integrating content and topic personalization to significantly boost student motivation and reach a more inclusive learning environment. This adds to the growing research about AI's potential in education as it indicates that these technologies can significantly enhance teaching and offer a more tailored and inclusive learning environment.

The dissertation is focused on exploring the aspects of the Accelerated Christian Education curriculum that shows its individualized nature. The aim of the study was to explore individualized instruction from the view of the ACE program with a view of possible recommendation for use on a wider scale in Nigerian schools. This was considered because of the several lapses identified in the present Nigerian education program. The research explored other teaching and learning methodologies to establish commonality and assess if indeed the programs form of individualization is related to any existing form. Interviews were conducted to get the experiences of students and supervisors who are using the program. Recommendations were made for consideration to the users of the program on the strengths and weaknesses examined and suggestions for possible improvement given based on the responses of the research participants. / M. Ed. (Didactics)

Chronic pain affects 20 % of the global population, causes suffering, is difficult to treat, and constitutes a large economic burden for society. So far, the characterization of molecular mechanisms of chronic pain-like behaviors in animal models has not translated into effective treatments. In this thesis, consisting of five studies, pain patient biofluids were analyzed with modern proteomic methods to identify biomarker candidates that can be used to improve our understanding of the pathophysiology chronic pain and lead to more effective treatments. Paper I is a proof of concept study, where a multiplex solid phase-proximity ligation assay (SP-PLA) was applied to cerebrospinal fluid (CSF) for the first time. CSF reference protein levels and four biomarker candidates for ALS were presented. The investigated proteins were not altered by spinal cord stimulation (SCS) treatment for neuropathic pain. In Paper II, patient CSF was explored by dimethyl and label-free mass spectrometric (MS) proteomic methods. Twelve proteins, known for their roles in neuroprotection, nociceptive signaling, immune regulation, and synaptic plasticity, were identified to be associated with SCS treatment of neuropathic pain. In Paper III, proximity extension assay (PEA) was used to analyze levels of 92 proteins in serum from patients one year after painful disc herniation. Patients with residual pain had significantly higher serum levels of 41 inflammatory proteins. In Paper IV, levels of 55 proteins were analyzed by a 100-plex antibody suspension bead array (ASBA) in CSF samples from two neuropathic pain patient cohorts, one cohort of fibromyalgia patients and two control cohorts. CSF protein profiles consisting of levels of apolipoprotein C1, ectonucleotide pyrophosphatase/phosphodiesterase family member 2, angiotensinogen, prostaglandin-H2 D-isomerase, neurexin-1, superoxide dismutases 1 and 3 were found to be associated with neuropathic pain and fibromyalgia. In Paper V, higher CSF levels of five chemokines and LAPTGF-beta-1were detected in two patient cohorts with neuropathic pain compared with healthy controls. In conclusion, we demonstrate that combining MS proteomic and multiplex antibody-based methods for analysis of patient biofluid samples is a viable approach for discovery of biomarker candidates for the pathophysiology and treatment of chronic pain. Several biomarker candidates possibly reflecting systemic inflammation, lipid metabolism, and neuroinflammation in different pain conditions were identified for further investigation. / Uppsala Berzelii Technology Centre for Neurodiagnostics