1,721,068 research outputs found
Clustering Trajectories to Study Diabetic Kidney Disease
Full text linkDiabetic kidney disease (DKD) is a serious complication of type-2 diabetes, defined prominently by a reduction in estimated glomerular filtration rate (eGFR), a measure of renal waste excretion capacity. However DKD patients present high heterogeneity in disease trajectory and response to treatment, making the one-model-fits-all pro- tocol for estimating prognosis and expected response to therapy as proposed by guidelines obsolete. As a solution, precision or stratified medicine aims to define subgroups of patients with similar pathophysi- ology and response to the therapy, allowing to select the best drug com- binations for each subgroup. We focus on eGFR when aiming to identify eGFR decline trends by clustering patients according to their eGFR tra- jectory shape-similarity.
The study involved 256 DKD patients observed annually for four years. Using the Fr ́echet distance, we built clusters of patients according to the similarity of their eGFR trajectories to identify distinct clusters. We formalized the trajectory-clustering approach through category the- ory. Characteristics of patients within different progression clusters were compared at the baseline and over time.
We identified five clusters of eGFR progression over time. We noticed a bifurcation of eGFR mean trajectories and a switch between two other mean trajectories. This particular clustering approach identified different mean eGFR trajectories. Our findings suggest the existence of distinct dynamical behaviors in the disease progression
Exploiting the Potential of Bayesian Networks in Deriving New Insight into Diabetic Kidney Disease (DKD)
Full text linkDiabetic kidney disease is a serious complication of diabetes and one of the leading causes of chronic and end-stage kidney disease worldwide. The clinical course and response to therapy is complex and heterogeneous both between and over time within individuals. Therefore it is extremely important to derive even more in-depth information on what characterizes its pathophysiology and pattern of disease progression. Statistical models can help in this task by understanding the interconnections among variables clinically considered to characterize the disease. In this work we propose to use Bayesian networks, a class of probabilistic graphical models, able to identify robust relationships among a set of variables. Furthermore, Bayesian networks are able to include expert knowledge in the modeling phase to reduce the uncertainty on the phenomenon under study. We provide some evidence that the synergy between data and expert prior information is a great source of valuable help in gaining new knowledge about Diabetic Kidney Disease
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
The Role of Different Immunosuppressants on Cellular Senescence in Renal Fibroblasts and Proximal Tubule Epithelial Cells
No full textAppropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The Role of Different Immunosuppressants on Cellular Senescence in Renal Fibroblasts and Proximal Tubule Epithelial Cells
No full textCandidate composite biomarker to inform drug treatments for diabetic kidney disease
Full text linkIntroduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.
Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.
Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment
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