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| http://dx.doi.org/10.26603/001c.117949 | DOI Listing |
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Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126, Torino, Italy.
Background And Objectives: Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.
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Shanghai Medical College, Fudan University, Shanghai, China.
Background: Isocitrate dehydrogenase (IDH)-mutant gliomas generally have a better prognosis than IDH-wild-type glioblastomas, and the extent of resection significantly impacts prognosis. However, there is a lack of integrated tools for predicting outcomes based on molecular subtypes and treatment modalities. This study aimed to identify factors influencing gross total resection (GTR) rates and to develop a clinical prognostic tool for IDH-mutant gliomas.
View Article and Find Full Text PDFImaging Biomarker Studies of Antipsychotic-Naïve First-Episode Schizophrenia in China: Progress and Future Directions.
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Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China.
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Evaluation of deceased-donor kidney offers: development and validation of novel data driven and expert based prediction models for early transplant outcomes.
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Department of Nephrology, University Hospital Heidelberg, Heidelberg, Germany.
In the face of growing transplant waitlists and aging donors, sound pre-transplant evaluation of organ offers is paramount. However, many transplant centres lack clear criteria on organ acceptance. Often, previous scores for donor characterisation have not been validated for the Eurotransplant population and are not established to support graft acceptance decisions.
View Article and Find Full Text PDFBiomarker Panels for Discriminating Risk of CKD Progression in Children.
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Department of Pediatrics, Division of Nephrology, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Background: We have previously studied biomarkers of tubular health (EGF), injury (KIM-1), dysfunction (alpha-1 microglobulin), and inflammation (TNFR-1, TNFR-2, MCP-1, YKL-40, suPAR), and demonstrated that plasma KIM-1, TNFR-1, TNFR-2 and urine KIM-1, EGF, MCP-1, urine alpha-1 microglobulin are each independently associated with CKD progression in children. In this study, we used bootstrapped survival trees to identify a combination of biomarkers to predict CKD progression in children.
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