Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 466

The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.

Quantum dots as a platform for nanoparticle drug delivery vehicle design

Open access
Probst, Christine E., Zrazhevskiy, Pavel, Bagalkot, Vaishali, Gao, Xiaohu·2013·Advanced Drug Delivery Reviews

Abstract

Nanoparticle-based drug delivery (NDD) has emerged as a promising approach to improving upon the efficacy of existing drugs and enabling the development of new therapies. Proof-of-concept studies have demonstrated the potential for NDD systems to simultaneously achieve reduced drug toxicity, improved bio-availability, increased circulation times, controlled drug release, and targeting. However, clinical translation of NDD vehicles with the goal of treating particularly challenging diseases, such as cancer, will require a thorough understanding of how nanoparticle properties influence their fate in biological systems, especially in vivo. Consequently, a model system for systematic evaluation of all stages of NDD with high sensitivity, high resolution, and low cost is highly desirable. In theory, this system should maintain the properties and behavior of the original NDD vehicle, while providing mechanisms for monitoring intracellular and systemic nanocarrier distribution, degradation, drug release, and clearance. For such a model system, quantum dots (QDots) offer great potential. QDots feature small size and versatile surface chemistry, allowing their incorporation within virtually any NDD vehicle with minimal effect on overall characteristics, and offer superb optical properties for real-time monitoring of NDD vehicle transport and drug release at both cellular and systemic levels. Though the direct use of QDots for drug delivery remains questionable due to their potential long-term toxicity, the QDot core can be easily replaced with other organic drug carriers or more biocompatible inorganic contrast agents (such as gold and magnetic nanoparticles) by their similar size and surface properties, facilitating translation of well characterized NDD vehicles to the clinic, maintaining NDD imaging capabilities, and potentially providing additional therapeutic functionalities such as photothermal therapy and magneto-transfection. In this review we outline unique features that make QDots an ideal platform for nanocarrier design and discuss how this model has been applied to study NDD vehicle behavior for diverse drug delivery applications.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.addr.2012.09.036

Cites

179 of 179 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 179 references

Cited by

See all 466

Metadata

Authors
Probst, C. E., Zrazhevskiy, P., Bagalkot, V., & Gao, X.
Venue
Advanced Drug Delivery Reviews
Publisher
Elsevier BV
Published
2013-05
Volume
65
Issue
5
Pages
703-718
ISSN
0169-409X (ISSN-L)
OpenAlex
W2088779155
Cited by
466
References
179
FWCI
1.24
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: Quantum dots as a platform for nanoparticle drug delivery vehicle design. https://app.cochat.ai/papers/10.1016/j.addr.2012.09.036
The paper
Probst, C. E., Zrazhevskiy, P., Bagalkot, V., & Gao, X. (2013). Quantum dots as a platform for nanoparticle drug delivery vehicle design. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2012.09.036

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.