The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
The acidic tumor microenvironment: a target for smart cancer nano-theranostics
Open accessAbstract
The acidic tumor microenvironment (TME), which mainly results from the high glycolytic rate of tumor cells, has been characterized as a hallmark of solid tumors and found to be a pivotal factor participating in tumor progression. Recently, due to the increasing understanding of the acidic TME, it has been shown that the acidic TME could be utilized as a multifaceted target during the design of various pH-responsive nanoscale theranostic platforms for the precise diagnosis and effective treatment of cancers. In this article, we will give a focused overview on the latest progress in utilizing this characteristic acidic TME as the target of nano-theranostics to enable cancer-specific imaging and therapy. The future perspectives in the development of acidic TME-targeting nanomedicine strategies will be discussed afterwards.
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
Principles of nanoparticle design for overcoming biological barriers to drug delivery
Blanco, E., Shen, H., & Ferrari, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3330 · Cited by 7,002
Stimuli-responsive nanocarriers for drug delivery
Mura, S., Nicolas, J., & Couvreur, P. (2013) · Nature Materials · doi:10.1038/nmat3776 · Cited by 6,289
Cancer nanomedicine: progress, challenges and opportunities
Shi, J., Kantoff, P. W., Wooster, R., & Farokhzad, O. C. (2017) · Nature Reviews Cancer · doi:10.1038/nrc.2016.108 · Cited by 5,873
Normalization of Tumor Vasculature: An Emerging Concept in Antiangiogenic Therapy
Jain, R. K. (2005) · Science · doi:10.1126/science.1104819 · Cited by 5,527
Regulation of cancer cell metabolism
Cairns, R. A., Harris, I. S., & Mak, T. W. (2011) · Nature Reviews Cancer · doi:10.1038/nrc2981 · Cited by 4,744
Photoacoustic tomography: In vivo imaging from organelles to organs
Wang, L. V., & Hu, S. (2012) · Science · doi:10.1126/science.1216210 · Cited by 4,331
The Effect of Nanoparticle Size, Shape, and Surface Chemistry on Biological Systems
Albanese, A., Tang, P. S., & Chan, W. C. W. (2012) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071811-150124 · Cited by 3,633
Thermo- and pH-responsive polymers in drug delivery☆
SCHMALJOHANN, D. (2006) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2006.09.020 · Cited by 3,023
Danhier, F., Feron, O., & Préat, V. (2010) · Journal of Controlled Release · doi:10.1016/j.jconrel.2010.08.027 · Cited by 2,800
The Fluorescent Toolbox for Assessing Protein Location and Function
Giepmans, B. N. G., Adams, S. R., Ellisman, M. H., & Tsien, R. Y. (2006) · Science · doi:10.1126/science.1124618 · Cited by 2,770
Cited by
Nam, S., & Mooney, D. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00798 · Cited by 764
Overcoming the translational barriers of tissue adhesives
Taboada, G. M., Yang, K., Pereira, M. J. N., Liu, S. S., Hu, Y., Karp, J. M., Artzi, N., & Lee, Y. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0171-7 · Cited by 449
Exosome engineering: Current progress in cargo loading and targeted delivery
Fu, S., Wang, Y., Xia, X., & Zheng, J. C. (2020) · NanoImpact · doi:10.1016/j.impact.2020.100261 · Cited by 444
Rational Design of Biomaterials to Potentiate Cancer Thermal Therapy
Zhu, Y., Li, Q., Wang, C., Hao, Y., Yang, N., Chen, M., Ji, J., Feng, L., & Liu, Z. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00822 · Cited by 163
Self-citationNarayan, R., Gadag, S., Cheruku, S. P., Raichur, A. M., Day, C. M., Garg, S., Manandhar, S., Pai, K. S. R., Suresh, A., Mehta, C. H., et al. (2021) · Carbohydrate Polymers · doi:10.1016/j.carbpol.2021.117893 · Cited by 103
Metadata
- Authors
- Feng, L., Dong, Z., Tao, D., Zhang, Y., & Liu, Z.
- Venue
- National Science Review
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-06-24
- Volume
- 5
- Issue
- 2
- Pages
- 269-286
- ISSN
- 2053-714X (ISSN-L)
- 2095-5138
- OpenAlex
- W2636501113
- Cited by
- 426
- References
- 136
- FWCI
- 8.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: The acidic tumor microenvironment: a target for smart cancer nano-theranostics. https://app.cochat.ai/papers/10.1093/nsr/nwx062
- The paper
- Feng, L., Dong, Z., Tao, D., Zhang, Y., & Liu, Z. (2018). The acidic tumor microenvironment: a target for smart cancer nano-theranostics. National Science Review. https://doi.org/10.1093/nsr/nwx062
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.
