856,322 research outputs found

    DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

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    Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

    Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

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    Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

    Leveraging Disease Progression Learning for Medical Image Recognition

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    Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning

    Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis

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    Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5(+) B lymphocytes in blood, bone marrow and lymphoid organs. CD1d-restricted invariant Natural Killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease both in EÎĽ;-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and iNKT cells critically delay the disease onset, but become functionally impaired upon disease progression. In patients, disease progression correlates also with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative/low CD1d expression on CLL cells and normal iNKT cells, suggesting an indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing Nurse Like Cells, a relevant pro-leukemia macrophage population. Finally, multivariate analysis identifies iNKT cell frequency as independent predictor of disease progression. Together, these results support iNKT cell contribution to CLL immune-surveillance and highlight iNKT cell frequency as prognostic marker for disease progression

    The role of changes in extracellular matrix of cartilage in the presence of inflammation on the pathology of osteoarthritis.

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    Osteoarthritis (OA) is a degenerative disease that affects various tissues surrounding joints such as articular cartilage, subchondral bone, synovial membrane, and ligaments. No therapy is currently available to completely prevent the initiation or progression of the disease partly due to poor understanding of the mechanisms of the disease pathology. Cartilage is the main tissue afflicted by OA, and chondrocytes, the sole cellular component in the tissue, actively participate in the degeneration process. Multiple factors affect the development and progression of OA including inflammation that is sustained during the progression of the disease and alteration in biomechanical conditions due to wear and tear or trauma in cartilage. During the progression of OA, extracellular matrix (ECM) of cartilage is actively remodeled by chondrocytes under inflammatory conditions. This alteration of ECM, in turn, changes the biomechanical environment of chondrocytes, which further drives the progression of the disease in the presence of inflammation. The changes in ECM composition and structure also prevent participation of mesenchymal stem cells in the repair process by inhibiting their chondrogenic differentiation. This review focuses on how inflammation-induced ECM remodeling disturbs cellular activities to prevent self-regeneration of cartilage in the pathology of OA

    Intraocular Pressure Fluctuation: Is It Important?

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    Elevated intraocular pressure (IOP) is a major risk factor for the development and progression of glaucoma. Previous prospective, randomized, long-term studies have demonstrated the strength of IOP reduction in slowing the progression of disease. It is well known that IOP is not a fixed value but fluctuates considerably over time. Although there have been some studies on IOP fluctuation and the progression of glaucoma, whether IOP fluctuation is an independent risk factor for glaucomatous damage and disease progression remains controversial. In this article, we reviewed the definition of IOP fluctuation, and both the evidence and the speculation for and against the effect of IOP fluctuation on glaucoma progression. Although conclusions seem to vary from study to study, we considered that different studies examined different groups of patients, at different stages of disease, and at different IOP levels. Our conclusion is that these apparently disparate results are not conflicting, but rather can be viewed as complementary. In clinical care, we recommend the consideration of IOP "modulation" rather than just IOP "reduction" when glaucoma patients are treated. Quality-based IOP control may be more effective than quantity-based IOP reduction to prevent or retard disease progression
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