The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
High Relaxivity Gadolinium Hydroxypyridonate−Viral Capsid Conjugates: Nanosized MRI Contrast Agents1
Abstract
High relaxivity macromolecular contrast agents based on the conjugation of gadolinium chelates to the interior and exterior surfaces of MS2 viral capsids are assessed. The proton nuclear magnetic relaxation dispersion (NMRD) profiles of the conjugates show up to a 5-fold increase in relaxivity, leading to a peak relaxivity (per Gd3+ ion) of 41.6 mM(-1) s(-1) at 30 MHz for the internally modified capsids. Modification of the exterior was achieved through conjugation to flexible lysines, while internal modification was accomplished by conjugation to relatively rigid tyrosines. Higher relaxivities were obtained for the internally modified capsids, showing that (i) there is facile diffusion of water to the interior of capsids and (ii) the rigidity of the linker attaching the complex to the macromolecule is important for obtaining high relaxivity enhancements. The viral capsid conjugated gadolinium hydroxypyridonate complexes appear to possess two inner-sphere water molecules (q = 2), and the NMRD fittings highlight the differences in the local motion for the internal (tauRl = 440 ps) and external (tauRl = 310 ps) conjugates. These results indicate that there are significant advantages of using the internal surface of the capsids for contrast agent attachment, leaving the exterior surface available for the installation of tissue targeting groups.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lipari, G., & Szabo, A. (1982) · Journal of the American Chemical Society · doi:10.1021/ja00381a009 · Cited by 3,777
The three-dimensional structure of the bacterial virus MS2
Valegård, K., Liljas, L., Fridborg, K., & Unge, T. (1990) · Nature · doi:10.1038/345036a0 · Cited by 411
Cited by
Villaraza, A. J. L., Bumb, A., & Brechbiel, M. W. (2010) · Chemical Reviews · doi:10.1021/cr900232t · Cited by 632
Metadata
- Authors
- Datta, A., Hooker, J. M., Botta, M., Francis, M. B., Aime, S., & Raymond, K. N.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-02-01
- Volume
- 130
- Issue
- 8
- Pages
- 2546-2552
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W1973534955
- Cited by
- 172
- References
- 2
- FWCI
- 9.77
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.