Relevant bibliographies by topics / Disease progression modeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Disease progression modeling'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Disease progression modeling"

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Reeve, Russell, Lei Pang, Bradley Ferguson, Michael O’Kelly, Seth Berry, and Wei Xiao. "Rheumatoid Arthritis Disease Progression Modeling." Therapeutic Innovation & Regulatory Science 47, no. 6 (November 2013): 641–50. http://dx.doi.org/10.1177/2168479013499571.

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Inoue, Lurdes Y. T., Ruth Etzioni, Christopher Morrell, and Peter Müller. "Modeling Disease Progression With Longitudinal Markers." Journal of the American Statistical Association 103, no. 481 (March 1, 2008): 259–70. http://dx.doi.org/10.1198/016214507000000356.

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Plevritis, Sylvia K. "Modeling disease progression in outcomes research." Academic Radiology 6 (January 1999): S132—S133. http://dx.doi.org/10.1016/s1076-6332(99)80108-1.

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Young, Alexandra L., Felix J. S. Bragman, Bojidar Rangelov, MeiLan K. Han, Craig J. Galbán, David A. Lynch, David J. Hawkes, et al. "Disease Progression Modeling in Chronic Obstructive Pulmonary Disease." American Journal of Respiratory and Critical Care Medicine 201, no. 3 (February 1, 2020): 294–302. http://dx.doi.org/10.1164/rccm.201908-1600oc.

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Rooney, William D., Yosef A. Berlow, William T. Triplett, Sean C. Forbes, Rebecca J. Willcocks, Dah-Jyuu Wang, Ishu Arpan, et al. "Modeling disease trajectory in Duchenne muscular dystrophy." Neurology 94, no. 15 (March 17, 2020): e1622-e1633. http://dx.doi.org/10.1212/wnl.0000000000009244.

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ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal changes in biomarkers were modeled with a cumulative distribution function using a nonlinear mixed-effects approach.ResultsMRS FF and MRI qT2 increased with DMD disease duration, with the progression time constants differing markedly between individuals and across muscles. The average age at half-maximal muscle involvement (μ) occurred 4.8 years earlier in vastus lateralis than soleus, and these measures were strongly associated with loss-of-ambulation age. Corticosteroid treatment was found to delay μ by 2.5 years on average across muscles, although there were marked differences between muscles with more slowly progressing muscles showing larger delay.ConclusionsMRS FF and MRI qT2 provide sensitive noninvasive measures of DMD progression. Modeling changes in these biomarkers across multiple muscles can be used to detect and monitor the therapeutic effects of corticosteroids on disease progression and to provide prognostic information on functional outcomes. This modeling approach provides a method to transform these MRI biomarkers into well-understood metrics, allowing concise summaries of DMD disease progression at individual and population levels.ClinicalTrials.gov identifier:NCT01484678.
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Zhou, Jiayu, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. "Modeling disease progression via multi-task learning." NeuroImage 78 (September 2013): 233–48. http://dx.doi.org/10.1016/j.neuroimage.2013.03.073.

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Mehdipour Ghazi, Mostafa, Mads Nielsen, Akshay Pai, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, and Lauge Sørensen. "Robust parametric modeling of Alzheimer’s disease progression." NeuroImage 225 (January 2021): 117460. http://dx.doi.org/10.1016/j.neuroimage.2020.117460.

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Sun, Zhaonan, Soumya Ghosh, Ying Li, Yu Cheng, Amrita Mohan, Cristina Sampaio, and Jianying Hu. "A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data." JAMIA Open 2, no. 1 (January 7, 2019): 123–30. http://dx.doi.org/10.1093/jamiaopen/ooy060.

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Abstract Objective Chronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management. Materials and Methods We developed a framework to build probabilistic disease progression models using observational medical data. The framework consists of two steps. The first step determines the number of disease states. The second step builds a probabilistic disease progression model with the determined number of states. The model discovers typical states along the trajectory of the target disease, learns the characteristics of these states, and transition probabilities between the states. We applied the framework to an integrated observational HD dataset curated from four recent observational HD studies. Results The resulting HD progression model identified nine disease states. Compared to state-of-art HD staging system, the model 1) covers wider range of HD progression; 2) is able to quantitatively describe complex changes around the time of clinical diagnosis; 3) discovers multiple potential HD progression pathways; and 4) reveals expected time durations of the identified states. Discussion and Conclusion The proposed framework addresses practical challenges in observational data and can help enhance the understanding of progression of chronic diseases. The framework could be applied to other chronic diseases with the help of clinical knowledge.
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Gomeni, Roberto, Monica Simeoni, Marina Zvartau-Hind, Michael C. Irizarry, Daren Austin, and Michael Gold. "Modeling Alzheimer's disease progression using the disease system analysis approach." Alzheimer's & Dementia 8, no. 1 (July 22, 2011): 39–50. http://dx.doi.org/10.1016/j.jalz.2010.12.012.

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Cook, Sarah F., and Robert R. Bies. "Disease Progression Modeling: Key Concepts and Recent Developments." Current Pharmacology Reports 2, no. 5 (August 15, 2016): 221–30. http://dx.doi.org/10.1007/s40495-016-0066-x.

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Dissertations / Theses on the topic "Disease progression modeling"

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Huszti, Ella. "Markov modeling of disease progression and mortality." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95060.

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Prognostic studies of progression and mortality in different diseases are essential to understand the role of particular prognostic factors and, thus, improve prognosis and ultimately help selecting appropriate interventions. Yet, such studies often face serious limitations of available data and/or of the existing statistical methods. One difficulty concerns separating the effects of putative prognostic factors on different clinical endpoints or “competing events” such as e.g. disease recurrence vs. recurrence-free death, or death due to disease vs. death due to other causes. This issue becomes even more challenging because data sources, such as cancer registries, often record only the date of death but not the cause of death. This can lead to bias in assessing the role of prognostic factors whose impact on the disease-specific mortality is quite different from their impact on all-cause mortality. It is important, therefore, to use methods that can deal accurately and efficiently with both (i) alternative pathways of disease progression, and (ii) unknown causes of death. The aforementioned challenges are addressed by 3 manuscripts. Previous empirical studies have suggested the potential advantages of using multi-state Markov models, over conventional time-to-event methods, to analyze competing risks and multi-state pathways of disease progression. In the 1st paper, I attempted to systematically assess, through a series of simulations, the performance of Markov models in this context and confirmed the accuracy of both point estimates of the regression coefficients and hypothesis tests. On the other hand, Relative Survival regression models have been developed, in the context of single-endpoint time-to-event analyses, to correct the regression coefficients for the unknown causes of death. Yet, no existing statistical model permits simultaneous combination of the advantages of both (i) Markov multi-state modeling, and (ii) Relative Survival. Therefore, in the
Les études pronostiques sur l'évolution et la mortalité de certaines pathologies sont essentielles pour comprendre le rôle de certains facteurs pronostiques et ainsi, améliorer le pronostic et finalement aider dans le choix des interventions thérapeutiques appropriées. Jusqu'à présent, les études de ce type ont été souvent confrontées à d'importantes limitations dans les données et/ou les méthodes statistiques disponibles. Une des difficultés concerne la discrimination, pour un même facteur pronostique, de ses effets sur différents critères cliniques ou événements concurrents, comme la récidive de la maladie vs le décès sans récidive, ou le décès dû à la pathologie vs le décès dû à d'autres causes. Ce problème devient d'autant plus important que les sources de données, comme les registres, enregistrent souvent uniquement la date de décès mais pas la cause. Ceci peut conduire à des biais dans l'évaluation du rôle des facteurs pronostiques dont l'effet sur la mortalité spécifique dû à la pathologie est différent de celui sur la mortalité toutes causes confondues. Il est donc important d'utiliser des méthodes qui puissent prendre en compte correctement à la fois (i) les différentes évolutions possibles de la pathologie et (ii) l'absence de la connaissance de la cause de décès. Les problèmes méthodologiques mentionnés précédemment sont traités dans 3 articles. Les études empiriques précédentes ont suggéré des avantages potentiels à utiliser les modèles multi-états de Markov à la place des modèles de survie conventionnels dans l'analyse des risques compétitifs et des différentes phases possibles d'évolution d'une pathologie. Le premier article tente d'évaluer méthodiquement, à l'aide de simulations, les performances des modèles de Markov dans ce contexte et confirme l'exactitude à la fois de l'estimation des coefficients de la régression et des tests d'hypothèse. D'un autre coté, les mod
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Sauty, Benoît. "Multimodal modelling of Alzheimer's Disease progression." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS348.

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La maladie d'Alzheimer (MA) est une pathologie multi-facette qui peut être surveillée grâce à une grande variété de modalités de données. Cette thèse vise à exploiter des données longitudinales multimodales, principalement des données d'imagerie et des tests cognitifs, pour fournir une description statistique de la progression de la MA et permettre une prévision individuelle de la dégradation future. Les modèles de progression à effet-mixtes de la maladie (DPMs) sont couramment utilisés pour ces tâches. Dans ce contexte, notre première contribution remet en question l'hypothèse fréquente selon laquelle les biomarqueurs suivent des fonctions linéaires ou logistiques au fil du temps, et nous proposons un cadre géométrique qui suppose que les données se trouvent sur une variété et suivent des géodésiques au fil du temps. Nous apprenons la métrique riemannienne de l'espace d'observation et sommes capables de modéliser une plus grande variété de biomarqueurs, sans hypothèses préalables sur la forme de la trajectoire au fil du temps. En utilisant des auto-encodeurs variationnels, nous étendons ensuite ce cadre aux données de neuroimagerie (IRM ou TEP), afin de fournir des modèles de progression en grande dimension qui décrivent les motifs d'altérations structurelles et fonctionnelles du cerveau au cours de la MA. Nous appliquons ensuite cette famille de DPMs à des données réelles afin d'étudier l'hétérogénéité de la progression de la MA, en décrivant l'influence du génotype APOE-e4 et du sexe sur les motifs d'altérations cérébrales. Enfin, nous utilisons ces DPMs avec un ensemble de biomarqueurs d'imagerie et extrait du fluide cérébrospinal pour identifier les combinaisons spécifiques de paramètres qui permettent de prévoir les déclins cognitifs chez les patients à différents stades de la maladie. La thèse démontre que les DPMs peuvent modéliser efficacement la progression de la MA en utilisant une grande variété de données longitudinales multimodales et fournir des informations précieuses sur les manifestations cliniques et la progression de la maladie. Ces résultats peuvent informer la conception d'essais cliniques et faciliter des stratégies de traitement individualisées et plus précises pour les patients atteints de la MA
Alzheimer's disease (AD) is a multi-facet pathology, that can be monitored through a variety of data types. This thesis aims to leverage multimodal longitudinal data, especially imaging scans and cognitive tests, to provide a statistical description of the progression of AD and to enable individual forecasting of future decline. Mixed-effect disease progression models (DPMs) are commonly used for these tasks. In this context, our first contribution questions the frequent assumption that biomarkers follow linear or logistic functions over time, and we propose a geometric framework that assumes the data lie on a manifold and follow geodesics over time. We learn the Riemannian metric of the observation space and are able to model a wider variety of biomarkers, without priors on the shape of the trajectory over time. Using variational auto-encoders, we then extend this framework to neuroimaging data (MRI or PET scans), in order to provide high-dimensional progression models that describe the patterns of structural and functional alterations of the brain over the course of AD. We then apply this family of DPMs to clinical studies data in order to investigate the heterogeneity of AD progression, due to APOE-e4 genotype and sex on patterns of brain alterations. Lastly, we use said DPMs with a set of imaging and fluid biomarkers to identify the specific combinations of input features that best forecast cognitive declines in patients at different stages of the disease. The thesis demonstrates that DPMs can effectively model the progression of AD using a great variety of multimodal longitudinal data and provide valuable insights into the disease's clinical manifestations and progression. These findings can inform clinical trial design and facilitate more accurate prognosis and individualized treatment strategies for patients with AD
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McHugh, Kevin J. "Age-related macular degeneration: interventional tissue engineering and predictive modeling of disease progression." Thesis, Boston University, 2014. https://hdl.handle.net/2144/19690.

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Thesis (Ph.D.)--Boston University
Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in people over the age of 50. As many as 50 million people are affected by AMD worldwide and prevalence is expected to continue to rise due to an aging population. There are two forms of the disease, dry (geographic atrophy) and wet (choroidal neovascularization), both of which result in retinal degeneration and central vision loss. Although anti-vascular endothelial growth factor therapies are moderately successful at treating the wet form, there are no treatments currently available for the more common dry form. Pharmacological therapies have been extensively explored for the treatment of dry AMD, but have achieved little success because the pathogenesis underlying AMD is unknown and likely varies among patients . Recently, tissue engineering has emerged as a promising approach to restore function by replacing diseased retinal tissue with healthy retinal pigment epithelium (RPE). While AMD-associated vision loss occurs when photoreceptors degenerate, this process arises as a consequence of earlier RPE dysfunction. In the healthy retina, the RPE acts as a critical regulator of the microenvironment for both photoreceptors and the nearby vasculature. However in AMD, the RPE no longer performs these essential homeostatic functions leading to photoreceptor apoptosis and vision loss. This dissertation describes the development and in vitro characterization of a tissue engineering scaffold for RPE delivery as potential treatment for dry AMD. First, a novel microfabrication-based method termed "pore casting" was developed to produce thin scaffolds with highly controlled pore size, shape, and spacing. Next, human RPE were cultured on pore-cast poly(c-caprolactone) (PCL) scaffolds and compared to cells on track-etched polyester, the standard RPE culture substrate. RPE on porous PCL demonstrated enhanced maturation and function compared to track-etched polyester including improved pigmentation, barrier formation, gene expression, growth factor secretion, and phagocytic degradation. Lastly, this study established a patient-specific method for predicting AMD progression using retinal oxygen concentration. This approach differs from current diagnosis techniques because it uses physiologically-relevant mechanisms rather than generalized clinical associations which have little, if any, prognostic value.
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Shelton, Morgan Griffin. "Modeling the Effects of Supercomplex Formation and Stress Response on Alzheimer’S Disease Progression." W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1563899025.

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Alzheimer’s disease is a specific form of dementia characterized by the aggregation of Amyloid-β plaques and tau tangles. New research has found that the formation of these aggregates occurs after dysregulation of respiratory activity and the production of radical oxygen species. Proteomic data shows that these changes are also related to unique gene expression patterns. We investigate the impact of these findings on new therapeutic options via metabolic flux analysis of sirtuin stress response pathways and respiratory supercomplex formation. Our results indicate CRISPR Cas-based gene therapy focused on upregulating stable CIII expression, and protective changes in SIRT1 and AMPK expression are potential avenues for therapeutics. This work also highlights the importance of metabolic enzyme activity in maintaining proper respiratory activity.
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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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Hubbard, Rebecca Allana. "Modeling a non-homogeneous Markov process via time transformation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9607.

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Bône, Alexandre. "Learning adapted coordinate systems for the statistical analysis of anatomical shapes. Applications to Alzheimer's disease progression modeling." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS273.

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Cette thèse construit des systèmes de coordonnées pour formes, c'est-à-dire des espaces métriques de dimension finie où les formes sont représentées par des vecteurs. Construire de tels systèmes de coordonnées permet de faciliter l'analyse statistique de collections de formes. Notre motivation finale est de prédire et de sous-typer la maladie d'Alzheimer, en se basant notamment sur des marqueurs ainsi extraits de banques d'images médicales du cerveau. Même si de telles banques sont longitudinales, la variabilité qu’elles renferment reste principalement due à la variabilité inter-individuelle importante et normale du cerveau. La variabilité due à la progression d’altérations pathologiques est d'une amplitude beaucoup plus faible. L'objectif central de cette thèse est de développer un système de coordonnées adapté pour l'analyse statistique de banques de données de formes longitudinales, capable de dissocier ces deux sources de variabilité. Comme montré dans la littérature, le transport parallèle peut être exploité pour obtenir une telle dissociation, par exemple en définissant la notion d’exp-parallélisme sur une variété. Utiliser cet outil sur un espace de formes s'accompagne cependant de défis théoriques et calculatoires, relevés dans la première partie de cette thèse. Enfin, si en anatomie computationnelle les espaces de formes sont communément équipés d'une structure de variété, les classes de difféomorphismes sous-jacentes sont le plus souvent construites sans tenir compte des données étudiées. Le dernier objectif majeur de cette thèse est de construire des systèmes de coordonnées de déformations où le paramétrage de ces déformations est adapté aux données d'intérêt
This thesis aims to build coordinate systems for shapes i.e. finite-dimensional metric spaces where shapes are represented by vectors. The goal of building such coordinate systems is to allow and facilitate the statistical analysis of shape data sets. The end-game motivation of our work is to predict and sub-type Alzheimer’s disease, based in part on knowledge extracted from banks of brain medical images. Even if these data banks are longitudinal, their variability remains mostly due to the large and normal inter-individual variability of the brain. The variability due to the progression of pathological alterations is of much smaller amplitude. The central objective of this thesis is to develop a coordinate system adapted for the statistical analysis of longitudinal shape data sets, able to disentangle these two sources of variability. As shown in the literature, the parallel transport operator can be leveraged to achieve this desired disentanglement, for instance by defining the notion of exp-parallel curves on a manifold. Using this tool on shape spaces comes however with theoretical and computational challenges, tackled in the first part of this thesis. Finally, if shape spaces are commonly equipped with a manifold-like structure in the field of computational anatomy, the underlying classes of diffeomorphisms are however most often largely built and parameterized without taking into account the data at hand. The last major objective of this thesis is to build deformation-based coordinate systems where the parameterization of deformations is adapted to the data set of interest
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Robertson, Chadia L. "Analysis of the Role of Astrocyte Elevated Gene-1 in Normal Liver Physiology and in the Onset and Progression of Hepatocellular Carcinoma." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3573.

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First identified over a decade ago, Astrocyte Elevated Gene-1 (AEG-1) has been studied extensively due to early reports of its overexpression in various cancer cell lines. Research groups all over the globe including our own have since identified AEG-1 overexpression in cancers of diverse lineages including cancers of the liver, colon, skin, prostate, breast, lung, esophagus, neurons and neuronal glia as compared to matched normal tissue. A comprehensive and convincing body of data currently points to AEG-1 as an essential component, critical to the progression and perhaps onset of cancer. AEG-1 is a potent activator of multiple pro-tumorigenic signal transduction pathways such as mitogen-activated protein extracellular kinase (MEK)/ extracellular signal-regulated kinase (ERK), phosphotidyl-inositol-3-kinase (PI3K)/Akt/mTOR, NF-κB and Wnt/β-catenin pathway. In addition, studies show that AEG-1 not only alters global gene and protein expression profiles, it also modulates fundamental intracellular processes, such as transcription, translation and RNA interference in cancer cells most likely by functioning as a scaffold protein. The mechanisms by which AEG-1 is overexpressed in cancer have been studied extensively and it is clear that multiple layers of regulation including genomic amplification, transcriptional, posttranscriptional, and posttranslational controls are involved however; the mechanism by which AEG 1 itself induces its oncogenic effects is still poorly understood. Just as questions remain about the exact role of AEG-1 in carcinogenesis, very little is known about the role of AEG-1 in regulating normal physiological functions in the liver. With the help of the Massey Cancer Center Transgenic/Knockout Mouse Core, our lab has successfully created a germline-AEG-1 knockout mouse (AEG-1-/-) as a model to interrogate AEG-1 function in vivo. Here I present the insights gained from efforts to analyze this novel AEG-1-/- mouse model. Aspects of the physiological functions of AEG-1 will be covered in chapter two wherein details of the characterization of the AEG-1-/- mouse are described including the role of AEG-1 in lipid metabolism. Chapter three discusses novel discoveries about the specific role of AEG-1 in mediating hepatocarcinogenesis by modulating NF-κB, a critical inflammatory pathway. First identified over a decade ago, Astrocyte Elevated Gene-1 (AEG-1) has been studied extensively due to early reports of its overexpression in various cancer cell lines. Research groups all over the globe including our own have since identified AEG-1 overexpression in cancers of diverse lineages including cancers of the liver, colon, skin, prostate, breast, lung, esophagus, neurons and neuronal glia as compared to matched normal tissue. A comprehensive and convincing body of data currently points to AEG-1 as an essential component, critical to the progression and perhaps onset of cancer. AEG-1 is a potent activator of multiple pro-tumorigenic signal transduction pathways such as mitogen-activated protein extracellular kinase (MEK)/ extracellular signal-regulated kinase (ERK), phosphotidyl-inositol-3-kinase (PI3K)/Akt/mTOR, NF-κB and Wnt/β-catenin pathway. In addition, studies show that AEG-1 not only alters global gene and protein expression profiles, it also modulates fundamental intracellular processes, such as transcription, translation and RNA interference in cancer cells most likely by functioning as a scaffold protein. The mechanisms by which AEG-1 is overexpressed in cancer have been studied extensively and it is clear that multiple layers of regulation including genomic amplification, transcriptional, posttranscriptional, and posttranslational controls are involved however; the mechanism by which AEG 1 itself induces its oncogenic effects is still poorly understood. Just as questions remain about the exact role of AEG-1 in carcinogenesis, very little is known about the role of AEG-1 in regulating normal physiological functions in the liver. With the help of the Massey Cancer Center Transgenic/Knockout Mouse Core, our lab has successfully created a germline-AEG-1 knockout mouse (AEG-1-/-) as a model to interrogate AEG-1 function in vivo. Here I present the insights gained from efforts to analyze this novel AEG-1-/- mouse model. Aspects of the physiological functions of AEG-1 will be covered in chapter two wherein details of the characterization of the AEG-1-/- mouse are described including the role of AEG-1 in lipid metabolism. Chapter three discusses novel discoveries about the specific role of AEG-1 in mediating hepatocarcinogenesis by modulating NF-κB, a critical inflammatory pathway.
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dePillis-Lindheim, Lydia. "Disease Correlation Model: Application to Cataract Incidence in the Presence of Diabetes." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/294.

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Diabetes is a major risk factor for the development of cataract [3,14,20,22]. In this thesis, we create a model that allows us to understand the incidence of one disease in the context of another; in particular, cataract in the presence of diabetes. The World Health Organization's Vision 2020 blindness-prevention initiative administers surgeries to remove cataracts, the leading cause of blindness worldwide [24]. One of the geographic areas most impacted by cataract-related blindness is Sub-Saharan Africa. In order to plan the number of surgeries to administer, the World Health Organization uses data on cataract prevalence. However, an estimation of the incidence of cataract is more useful than prevalence data for the purpose of resource planning. In 2012, Dray and Williams developed a method for estimating incidence based on prevalence data [5]. Incidence estimates can be further refined by considering associated risk factors such as diabetes. We therefore extend the Dray and Williams model to include diabetes prevalence when calculating cataract incidence estimates. We explore two possible approaches to our model construction, one a detailed extension, and the other, a simplification of that extension. We provide a discussion comparing the two approaches.
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Laranjeira, Simão. "Modelling the progression of neurodegenerative diseases." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:ebb621d0-e4e6-405e-9e54-ba385c3ebd0a.

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Neurodegenerative disease is an umbrella term for pathologies that primarily damage neurons. As their incidence increases with age it is becoming of a greater concern for the west, due to its aging population. Due to their chronic nature and the difficulty to create reliable and reproducible animal models of these diseases their pathophysiologies are still poorly understood. For all these reasons, a mathematical modelling approach is suggested. The methodology of the work here consisted of identifying the state of the art models that describe the healthy behaviour of cells (e.g. metabolism and ionic regulation) and adapting them for pathological environments. With these models hypotheses provided by clinicians and pathologists were tested. The work focuses on developing models of mechanisms common to neurodegenerative diseases, which include: glutamate excitotoxicity, aquaporin water kinetics, inflammatory complement lysis and acute inflammation. Glutamate excitotoxicity was modelled by creating a compartmental model of glutamate exchange between neurons and astrocytes. This model was the first model of glutamate kinetics validated in an ischaemic stroke context. The aquaporin water kinetics and complement lysis models were developed in the context of the autoimmune disease Neuromyelitis Optica. Through this project a hypothesised trigger for the pathology was confirmed. Additionally, the first model of astrocytic cytotoxic oedema due to complement lysis was developed. Finally, a preventative drug for complement lysis was simulated. Acute inflammation was explored in the context of understanding the potential of chemerin as a pro-resolving cytokine. To that effect, a model of acute inflammation was developed where pro-resolving mechanisms were included. This model was the first to attempt model the effects of an intervention in inflammation. The results indicated that there is a maximum inhibitory effect of chemerin on inflammation. Additionally, two preventive avenues for chronic inflammation were found. With this work, the first attempts of capturing relevant mechanisms of neurodegenerative diseases were presented. These models can now be further developed and adapted to other pathological environments.
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Books on the topic "Disease progression modeling"

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Aspden, Richard, and Jenny Gregory. Morphology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0011.

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The study of joint morphology can help us to understand the risk factors for osteoarthritis (OA), how it progresses, and aids in developing imaging biomarkers for study of the disease. OA results in gross structural changes in affected joints. Growth of osteophytes, deformation of joint components, and loss of joint space where cartilage has broken down are all characteristics of the disorder. Certain bone shapes as well as malalignment predispose people to future OA, or may be a marker for early OA. Geometrical measures, such as the alpha angle or Wiberg’s CE angle, used to be the primary tool for investigating morphology. In recent years, however, statistical shape modelling (SSM) has become increasingly popular. SSM can be used with any imaging modality and has been successfully applied to a number of musculoskeletal conditions. It uses sets of landmark points denoting the anatomy of one or more bones to generate new variables (modes) that describe and quantify the shape variation in a set of images via principal components analysis. With the aid of automated search algorithms for point placement, the use of SSMs is expanding and provides a valuable and versatile tool for exploration of bone and joint morphometry. Whilst the majority of research has focused on hip and knee OA, this chapter provides an overview of joint morphology through the whole skeleton and how it has helped our ability to understand and quantify the risk and progression of osteoarthritis.
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Book chapters on the topic "Disease progression modeling"

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Camargo, Anyela, and Jan T. Kim. "Disease Progression Modeling." In Encyclopedia of Systems Biology, 582. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_221.

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Ibarra, Manuel, Marianela Lorier, and Iñaki F. Trocóniz. "Pharmacometrics: Disease Progression Modeling." In The ADME Encyclopedia, 939–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84860-6_174.

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Ibarra, Manuel, Marianela Lorier, and Iñaki F. Trocóniz. "Pharmacometrics: Disease Progression Modeling." In The ADME Encyclopedia, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_174-1.

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Ng, Kenney, Mohamed Ghalwash, Prithwish Chakraborty, Daby M. Sow, Akira Koseki, Hiroki Yanagisawa, and Michiharu Kudo. "Data-Driven Disease Progression Modeling." In Health Informatics, 247–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07912-2_17.

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Mould, Diane R. "Modeling the Progression of Disease." In Pharmacokinetics in Drug Development, 57–90. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7937-7_3.

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Oxtoby, Neil P. "Data-Driven Disease Progression Modeling." In Machine Learning for Brain Disorders, 511–32. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_17.

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AbstractIntense debate in the neurology community before 2010 culminated in hypothetical models of Alzheimer’s disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modeling emerged from the computer science community with the aim to reconstruct neurodegenerative disease timelines using data from large cohorts of patients, healthy controls, and prodromal/at-risk individuals. This chapter describes selected highlights from the field, with a focus on utility for understanding and forecasting of disease progression.
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Self, Steve, and Yudi Pawitan. "Modeling a Marker of Disease Progression and Onset of Disease." In AIDS Epidemiology, 231–55. Boston, MA: Birkhäuser Boston, 1992. http://dx.doi.org/10.1007/978-1-4757-1229-2_11.

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Venkatraghavan, Vikram, Esther E. Bron, Wiro J. Niessen, and Stefan Klein. "A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling." In Lecture Notes in Computer Science, 121–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59050-9_10.

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Ley-Chavez, Adriana, and Julia L. Higle. "MODELING DISEASE PROGRESSION AND RISK-DIFFERENTIATED SCREENING FOR CERVICAL CANCER PREVENTION." In Decision Analytics and Optimization in Disease Prevention and Treatment, 153–82. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781118960158.ch7.

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Rivail, Antoine, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu, and Hrvoje Bogunovic. "Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning." In Predictive Intelligence in Medicine, 44–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6_5.

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Conference papers on the topic "Disease progression modeling"

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Yang, Xi, Ge Gao, and Min Chi. "Hierarchical Apprenticeship Learning for Disease Progression Modeling." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/265.

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Disease progression modeling (DPM) plays an essential role in characterizing patients' historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to induce decision-making policies by observing and imitating expert behaviors. In this paper, we investigate the incorporation of AL-derived patterns into DPM, utilizing a Time-aware Hierarchical EM Energy-based Subsequence (THEMES) AL approach. To the best of our knowledge, this is the first study incorporating AL-derived progressive and interventional patterns for DPM. We evaluate the efficacy of this approach in a challenging task of septic shock early prediction, and our results demonstrate that integrating the AL-derived patterns significantly enhances the performance of DPM.
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Wang, Xulong, Jun Qi, Yun Yang, and Po Yang. "A Survey of Disease Progression Modeling Techniques for Alzheimer's Diseases." In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). IEEE, 2019. http://dx.doi.org/10.1109/indin41052.2019.8972091.

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Pearson, Ronald K., Robert J. Kingan, and Alan Hochberg. "Disease progression modeling from historical clinical databases." In Proceeding of the eleventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1081870.1081974.

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Sukkar, R., E. Katz, Yanwei Zhang, D. Raunig, and B. T. Wyman. "Disease progression modeling using Hidden Markov Models." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346556.

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Liu, Xiaoli, Jiali Li, and Peng Cao. "Modeling Disease Progression with Deep Neural Networks." In ISICDM 2020: The Fourth International Symposium on Image Computing and Digital Medicine. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3451421.3451429.

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Zhou, Jiayu, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. "Modeling disease progression via fused sparse group lasso." In the 18th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339702.

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Zheng, Kaiping, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, and Wei Luen James Yip. "Capturing Feature-Level Irregularity in Disease Progression Modeling." In CIKM '17: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132847.3132944.

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Jeong, Seungwoo, Wonsik Jung, Junghyo Sohn, and Heung-Il Suk. "Deep Geometrical Learning for Alzheimer’s Disease Progression Modeling." In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00031.

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Roberts, Michael D., Ian A. Sigal, Yi Liang, Claude F. Burgoyne, and J. Crawford Downs. "Finite Element Modeling of the Connective Tissues of the Optic Nerve Head in Bilaterally Normal Monkeys." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206522.

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Glaucoma is a chronic and progressive optic neuropathy that gradually narrows the field of vision and can culminate in blindness. Despite extensive and prolonged research efforts, the mechanisms that initiate and fuel progression of the disease are not well understood. However, reduction of intraocular pressure (IOP) has been shown to be an effective therapy for slowing glaucomatous progression, although the specific role of IOP in the disease is not well understood.
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Zhou, Menghui, Xulong Wang, Yun Yang, Fengtao Nan, Yu Zhang, Jun Qi, and Po Yang. "Modeling Disease Progression Flexibly with Nonlinear Disease Structure via Multi-task Learning." In 2021 17th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2021. http://dx.doi.org/10.1109/msn53354.2021.00063.

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Reports on the topic "Disease progression modeling"

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Barhak, Jacob. Supplemental Information: The Reference Model is a Multi-Scale Ensemble Model of COVID-19. Outbreak, May 2021. http://dx.doi.org/10.34235/b7eaa32b-1a6b-444f-9848-76f83f5a733c.

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The COVID-19 pandemic has accelerated research worldwide and resulted in a large number of computational models and initiatives. Models were mostly aimed at forecast and resulted in different predictions partially since models were based on different assumptions. In fact the idea that a computational model is just an assumption attempting to explain a phenomenon has not been sufficiently explored. Moreover, the ability to combine models has not been fully realized. The Reference Model for disease progression was performing this task for years for diabetes models and recently started modeling COVID-19. The Reference Model is an ensemble of models that is optimized to fit observed disease phenomenon. The ensemble has the ability to include model components from different sources that compete and cooperate. The recent advance in this model is the ability to include models calculated in different scales, making the model the first known multi-scale ensemble model. This manuscript will review these capabilities and show how multiple models can improve our ability to comprehend the COVID-19 pandemic.
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowledge representation for engineered system state that encompasses components and environmental factors. Epigenetics is concerned with explaining how environmental factors affect the expression of an organism’s genetic material. The field has derived important in-sights into the development and progression of disease states based on how environmental factors impact genetic material, causing variations in how a gene is expressed. The health of an engineered system is similarly influenced by its environment. A foundation for a new approach to prognostics based on epigenetics must begin by representing the entities and relationships of an engineered system from the perspective of epigenetics. This paper presents an ontology for an epigenetics-inspired representation of an engineered system. An ontology describing the epigenetics of an engineered system will enable the composition of a formal model and the incremental development of a more robust, causal reasoning system.
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