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Updated: Feb 1, 2026

Imaging Mycobacterium tuberculosis in Mice with Reporter Enzyme Fluorescence
Published on: February 26, 2018
Building capacity for advances in tuberculosis research; proceedings of the third RePORT international meeting
Yuri F van der Heijden1, Fareed Abdullah2, Bruno B Andrade3
1Vanderbilt Tuberculosis Center, Vanderbilt University School of Medicine, Nashville, TN, USA; Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
Abstract:
RePORT International is a global network of research sites in India, Brazil, Indonesia, South Africa, China, and the Philippines dedicated to collaborative tuberculosis research in the context of HIV. A standardized research protocol (the Common Protocol) guides the enrollment of participants with active pulmonary tuberculosis and contacts into observational cohorts. The establishment of harmonized clinical data and bio-repositories will allow cutting-edge, large-scale advances in the understanding of tuberculosis, including identification of novel biomarkers for progression to active tuberculosis and relapse after treatment. The RePORT International infrastructure aims to support research capacity development through enabling globally-diverse collaborations. To that end, representatives from the RePORT International network sites, funding agencies, and other stakeholders gathered together in Brazil in September 2017 to present updates on relevant research findings and discuss ideas for collaboration. Presenters emphasized research involving biomarker identification for incipient tuberculosis, host immunity and pharmacogenomics, co-morbidities such as HIV and type 2 diabetes mellitus, and tuberculosis transmission in vulnerable and high-risk populations. Currently, 962 active TB participants and 670 household contacts have contributed blood, sputum, urine and microbes to in-country biorepositories. Cross-consortium collaborations have begun sharing data and specimens to analyze molecular and cytokine predictive patterns.
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