The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Nanoparticulate Assemblies of Amphiphiles and Diagnostically Active Materials for Multimodality Imaging
Open accessAbstract
Modern medicine has greatly benefited from recent dramatic improvements in imaging techniques. The observation of physiological events through interactions manipulated at the molecular level offers unique insight into the function (and dysfunction) of the living organism. The tremendous advances in the development of nanoparticulate molecular imaging agents over the past decade have made it possible to noninvasively image the specificity, pharmacokinetic profiles, biodistribution, and therapeutic efficacy of many novel compounds. Several types of nanoparticles have demonstrated utility for biomedical purposes, including inorganic nanocrystals, such as iron oxide, gold, and quantum dots. Moreover, natural nanoparticles, such as viruses, lipoproteins, or apoferritin, as well as hybrid nanostructures composed of inorganic and natural nanoparticles, have been applied broadly. However, among the most investigated nanoparticle platforms for biomedical purposes are lipidic aggregates, such as liposomal nanoparticles, micelles, and microemulsions. Their relative ease of preparation and functionalization, as well as the ready synthetic ability to combine multiple amphiphilic moieties, are the most important reasons for their popularity. Lipid-based nanoparticle platforms allow the inclusion of a variety of imaging agents, ranging from fluorescent molecules to chelated metals and nanocrystals. In recent years, we have created a variety of multifunctional lipid-based nanoparticles for molecular imaging; many are capable of being used with more than one imaging technique (that is, with multimodal imaging ability). These nanoparticles differ in size, morphology, and specificity for biological markers. In this Account, we discuss the development and characterization of five different particles: liposomes, micelles, nanocrystal micelles, lipid-coated silica, and nanocrystal high-density lipoprotein (HDL). We also demonstrate their application for multimodal molecular imaging, with the main focus on magnetic resonance imaging (MRI), optical techniques, and transmission electron microscopy (TEM). The functionalization of the nanoparticles and the modulation of their pharmacokinetics are discussed. Their application for molecular imaging of key processes in cancer and cardiovascular disease are shown. Finally, we discuss a recent development in which the endogenous nanoparticle HDL was modified to carry different diagnostically active nanocrystal cores to enable multimodal imaging of macrophages in experimental atherosclerosis. The multimodal characteristics of the different contrast agent platforms have proven to be extremely valuable for validation purposes and for understanding mechanisms of particle-target interaction at different levels, ranging from the entire organism down to cellular organelles.
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
Nanocarriers as an emerging platform for cancer therapy
Peer, D., Karp, J. M., Hong, S., Farokhzad, O. C., Margalit, R., & Langer, R. (2007) · Nature Nanotechnology · doi:10.1038/nnano.2007.387 · Cited by 8,710
Recent advances with liposomes as pharmaceutical carriers
Torchilin, V. P. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1632 · Cited by 5,282
In vivo tumor targeting and spectroscopic detection with surface-enhanced Raman nanoparticle tags
Qian, X., Peng, X.-H., Ansari, D. O., Yin-Goen, Q., Chen, G. Z., Shin, D. M., Yang, L., Young, A. N., Wang, M. D., & Nie, S. (2008) · Nature Biotechnology · doi:10.1038/nbt1377 · Cited by 2,201
Nanocrystal Core High-Density Lipoproteins: A Multimodality Contrast Agent Platform
Cormode, D. P., Skajaa, T., van Schooneveld, M. M., Koole, R., Jarzyna, P., Lobatto, M. E., Calcagno, C., Barazza, A., Gordon, R. E., Zanzonico, P., et al. (2008) · Nano Letters · doi:10.1021/nl801958b · Cited by 328
Self-citation
Cited by
Nanochemistry and Nanomedicine for Nanoparticle-based Diagnostics and Therapy
Chen, G., Roy, I., Yang, C., & Prasad, P. N. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00148 · Cited by 1,499
Synthesis, Functionalization, and Biomedical Applications of Multifunctional Magnetic Nanoparticles
Hao, R., Xing, R., Xu, Z., Hou, Y., Gao, S., & Sun, S. (2010) · Advanced Materials · doi:10.1002/adma.201000260 · Cited by 1,372
Mesoporous silica nanoparticles in drug delivery and biomedical applications
Wang, Y., Zhao, Q., Han, N., Bai, L., Li, J., Liu, J., Che, E., Hu, L., Zhang, Q., Jiang, T., et al. (2015) · Nanomedicine: Nanotechnology, Biology and Medicine · doi:10.1016/j.nano.2014.09.014 · Cited by 1,192
Nanomaterials for In Vivo Imaging
Smith, B. R., & Gambhir, S. S. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00073 · Cited by 1,075
Multimodality Imaging Probes: Design and Challenges
Louie, A. (2010) · Chemical Reviews · doi:10.1021/cr9003538 · Cited by 1,044
Metadata
- Authors
- Mulder, W. J. M., Strijkers, G. J., van Tilborg, G. A. F., Cormode, D. P., Fayad, Z. A., & Nicolay, K.
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-05-12
- Volume
- 42
- Issue
- 7
- Pages
- 904-914
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W2024664318
- Cited by
- 267
- References
- 4
- FWCI
- 15.04
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Nanoparticulate Assemblies of Amphiphiles and Diagnostically Active Materials for Multimodality Imaging. https://app.cochat.ai/papers/10.1021/ar800223c
- The paper
- Mulder, W. J. M., Strijkers, G. J., van Tilborg, G. A. F., Cormode, D. P., Fayad, Z. A., & Nicolay, K. (2009). Nanoparticulate Assemblies of Amphiphiles and Diagnostically Active Materials for Multimodality Imaging. Accounts of Chemical Research. https://doi.org/10.1021/ar800223c
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.