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Usage Information

Visualizing the dynamics of tuberculosis pathology using molecular imaging
Alvaro A. Ordonez, … , Laura E. Via, Sanjay K. Jain
Alvaro A. Ordonez, … , Laura E. Via, Sanjay K. Jain
Published March 1, 2021
Citation Information: J Clin Invest. 2021;131(5):e145107. https://doi.org/10.1172/JCI145107.
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Review

Visualizing the dynamics of tuberculosis pathology using molecular imaging

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Abstract

Nearly 140 years after Robert Koch discovered Mycobacterium tuberculosis, tuberculosis (TB) remains a global threat and a deadly human pathogen. M. tuberculosis is notable for complex host-pathogen interactions that lead to poorly understood disease states ranging from latent infection to active disease. Additionally, multiple pathologies with a distinct local milieu (bacterial burden, antibiotic exposure, and host response) can coexist simultaneously within the same subject and change independently over time. Current tools cannot optimally measure these distinct pathologies or the spatiotemporal changes. Next-generation molecular imaging affords unparalleled opportunities to visualize infection by providing holistic, 3D spatial characterization and noninvasive, temporal monitoring within the same subject. This rapidly evolving technology could powerfully augment TB research by advancing fundamental knowledge and accelerating the development of novel diagnostics, biomarkers, and therapeutics.

Authors

Alvaro A. Ordonez, Elizabeth W. Tucker, Carolyn J. Anderson, Claire L. Carter, Shashank Ganatra, Deepak Kaushal, Igor Kramnik, Philana L. Lin, Cressida A. Madigan, Susana Mendez, Jianghong Rao, Rada M. Savic, David M. Tobin, Gerhard Walzl, Robert J. Wilkinson, Karen A. Lacourciere, Laura E. Via, Sanjay K. Jain

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Usage data is cumulative from August 2024 through August 2025.

Usage JCI PMC
Text version 1,089 291
PDF 123 49
Figure 178 7
Table 174 0
Citation downloads 80 0
Totals 1,644 347
Total Views 1,991
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Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

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