Molecular Imaging of Bacterial Infections in vivo: The Discrimination between Infection and Inflammation
Abstract
:1. Clinical Approach to Identification of Infection
Labeling Agent | Modality | Half-Life | Pre-Clinical | Clinical (FDA approved) |
---|---|---|---|---|
99Technetium | SPECT | 6 h | ✓ | ✓ |
89Zirconium | PET | 3.3 days | ✓ | ✓ |
67 or 68Gallium salts | PET, SPECT | 3.26 days; 68 min | ✓ | ✓ |
111Indium | SPECT | 2.8 days | ✓ | ✓ |
64Copper (II) | PET | 12.7 h | ✓ | ✓ |
18Fluorine | PET | 109.8 min | ✓ | ✓ |
123,124,125,131Iodine | PET, SPECT | 13.3 h; 4.18 days; 59.4 days; 8 days | ✓ | ✓ |
Superparamagnetic iron oxide nanoparticles (SPIO) | MRI | N/A | ✓ | ✓ |
Cross linked iron oxide nanoparticles (CLIO) | MRI | N/A | ✓ | ✓ |
Monocrystalline iron oxide nanoparticles (MION) | MRI | N/A | ✓ | ✓ |
Gadolinium-Chelator (DOTA, DPTA, etc.) | MRI | 1.5 h | ✓ | ✓ |
Colloidal Quantum Dots | Optical | N/A | ✓ | × |
Near-Infrared Fluorophore (NIRF): Indocyanine green | Optical | 150–180 s (blood) | ✓ | ✓ |
Cyanine5, 5.5,7; AlexaFluor dyes | Optical | N/A | ✓ | × |
Bioluminescence | Optical | N/A | ✓ | × |
2. Separating Inflammation from Infection
2.1. Indirect Detection of Leukocytes
2.1.1. Integrins and Selectins
2.1.2. Myeloperoxidase
2.2. Detection of Myeloid Cells
2.2.1. Monocytes and Macrophages
2.2.2. Neutrophils
2.3. Adaptive Immunity
2.3.1. Dendritic Cells
2.3.2. T Cells
2.3.3. B Cells
2.4. Small Molecule Imaging
2.4.1. Chemokines
2.4.2. Proteases
2.4.3. Caspases
3. Imaging of Infectious Species
3.1. Labeled Antimicrobials for Detection of Infection
Synthetic and Endogenous Antibiotics
3.2. Pathogen Specific Targets
3.2.1. Cell Wall
3.2.2. Bacterial Specific Factors
4. Endocarditis: A Model of Difficult Diagnosis
Clinical Tracer | Imaging Modality | Sample Size | Sensitivity | Specificity | Positive and negative predictive values | Evaluated and Recommended for Endocarditis |
18F-FDG | PET/CT | 72 | 39% | 93% | 64%
82% | × |
99mTc-HMPAO-WBC | SPECT/CT | 51 | 90% | 100% | 100%
94% | ✓ |
99mTc-anti-NCA-95 | Scintigraphy SPECT | 72 | 79% | 82% | × | ✓ |
111In-DPTA-anti-Fibrin mAb | Scintigraphy | 86 | 97% | 72% | × | N/A |
99m-Tc-DTPA-anti-Fibrin mAb | Scintigraphy | 94 | 84.2% | 97.6% | × | N/A |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Eggleston, H.; Panizzi, P. Molecular Imaging of Bacterial Infections in vivo: The Discrimination between Infection and Inflammation. Informatics 2014, 1, 72-99. https://doi.org/10.3390/informatics1010072
Eggleston H, Panizzi P. Molecular Imaging of Bacterial Infections in vivo: The Discrimination between Infection and Inflammation. Informatics. 2014; 1(1):72-99. https://doi.org/10.3390/informatics1010072
Chicago/Turabian StyleEggleston, Heather, and Peter Panizzi. 2014. "Molecular Imaging of Bacterial Infections in vivo: The Discrimination between Infection and Inflammation" Informatics 1, no. 1: 72-99. https://doi.org/10.3390/informatics1010072
APA StyleEggleston, H., & Panizzi, P. (2014). Molecular Imaging of Bacterial Infections in vivo: The Discrimination between Infection and Inflammation. Informatics, 1(1), 72-99. https://doi.org/10.3390/informatics1010072