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Cited by 355

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

H2S‐Activable MOF Nanoparticle Photosensitizer for Effective Photodynamic Therapy against Cancer with Controllable Singlet‐Oxygen Release

Ma, Yu, Li, Xiangyuan, Li, Aijie, Yang, Peng, Zhang, Caiyun, Tang, Bo·2017·Angewandte Chemie International Edition

Abstract

Photodynamic therapy (PDT) has emerged as an important minimally invasive tumor treatment technology. The search for an effective photosensitizer to realize selective cancer treatment has become one of the major foci in recent developments of PDT technology. Controllable singlet‐oxygen release based on specific cancer‐associated events, as another major layer of selectivity mode, has attracted great attention in recent years. Here, for the first time, we demonstrated that a novel mixed‐metal metal–organic framework nanoparticle (MOF NP) photosensitizer can be activated by a hydrogen sulfide (H 2 S) signaling molecule in a specific tumor microenvironment for PDT against cancer with controllable singlet‐oxygen release in living cells. The effective removal of tumors in vivo further confirmed the satisfactory treatment effect of the MOF NP photosensitizer.

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Metadata

Authors
Ma, Y., Li, X., Li, A., Yang, P., Zhang, C., & Tang, B.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2017-09-22
Volume
56
Issue
44
Pages
13752-13756
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2753556542
Cited by
355
References
28
FWCI
19.44
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: H2S‐Activable MOF Nanoparticle Photosensitizer for Effective Photodynamic Therapy against Cancer with Controllable Singlet‐Oxygen Release. https://app.cochat.ai/papers/10.1002/anie.201708005
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Ma, Y., Li, X., Li, A., Yang, P., Zhang, C., & Tang, B. (2017). H2S‐Activable MOF Nanoparticle Photosensitizer for Effective Photodynamic Therapy against Cancer with Controllable Singlet‐Oxygen Release. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201708005

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.