Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Serological Proteomic Screening And Evaluation Of A Recombinant Egg Antigen For The Diagnosis Of Low-Intensity Schistosoma Mansoni Infections In Endemic Area In Brazil, Vanessa Silva-Moraes, Lisa Marie Shollenberger, William Castro-Borges, Ana Lucia Teles Rabello, Donald A. Harn, Lia Carolina Soares Medeiros, Wander De Jesus Jeremias, Liliane Maria Vidal Siqueira, Caroline Stephane Salviano Pereira, Maria Luysa Camargos Pedrosa, Nathalie Bonatti Franco Almeida, Aureo Almeida, Jose Roberto Lambertucci, Nidia Francisca De Figueiredo Carneiro, Paulo Marcos Zech Coelho, Refaella Fortini Queiroz Grenfell

Biological Sciences Faculty Publications

Background

Despite decades of use of control programs, schistosomiasis remains a global public health problem. To further reduce prevalence and intensity of infection, or to achieve the goal of elimination in low-endemic areas, there needs to be better diagnostic tools to detect low-intensity infections in low-endemic areas in Brazil. The rationale for development of new diagnostic tools is that the current standard test Kato-Katz (KK) is not sensitive enough to detect low-intensity infections in low-endemic areas. In order to develop new diagnostic tools, we employed a proteomics approach to identify biomarkers associated with schistosome-specific immune responses in hopes of developing …

Feature-Guided Deep Radiomics For Glioblastoma Patient Survival Prediction, Zeina A. Shboul, Mahbubul Alam, Lasitha Vidyaratne, Linmin Pei, Mohamed I. Elbakary, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed …