Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 313

The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.

Recent Progress in NIR-II Contrast Agent for Biological Imaging

Open access
Cao, Jie, Zhu, Binling, Zheng, Kefang, He, Songguo, Meng, Liang, Song, Jibin, Yang, Huanghao·2020·Frontiers in Bioengineering and Biotechnology

Abstract

Fluorescence imaging technology has gradually become a new and promising tool for in vivo visualization detection. Because it can provide real-time sub-cellular resolution imaging results, it can be widely used in the field of biological detection and medical detection and treatment. However, due to the limited imaging depth (1-2 mm) and self-fluorescence background of tissue emitted in the visible region (400-700 nm), it fails to reveal biological complexity in deep tissues. The traditional near infrared wavelength (NIR-I, 700-950 nm) is considered as the first biological window, because it reduces the NIR absorption and scattering from blood and water in organisms. NIR fluorescence bioimaging’s penetration is larger than that of visible light. In fact, NIR-I fluorescence bioimaging is still interfered by tissue autofluorescence (background noise), and the existence of photon scattering, which limits the depth of tissue penetration. Recent experimental and simulation results show that the signal-to-noise ratio (SNR) of bioimaging can be significantly improved at the second region near infrared (NIR-II, 1000-1700 nm), also known as the second biological window. NIR-II bioimaging is able to explore deep-tissues information in the range of centimeter, and to obtain micron-level resolution at the millimeter depth, which surpass the performance of NIR-I fluorescence imaging. The key of fluorescence bioimaging is to achieve highly selective imaging thanks to the functional/targeting contrast agent (probe). However, the progress of NIR-II probes is very limited. To date, there only are few reports about NIR-II fluorescence probes, such as carbon nanotubes, Ag2S quantum dots and organic small molecular dyes. In this paper, we surveyed the development of NIR-II imaging contrast agents and their application in cancer imaging, medical detection, vascular bioimaging and cancer diagnosis. In addition, the hotspots and challenges of NIR-II bioimaging are discussed. It is expected that our findings will lay a foundation for further theoretical research and practical application of NIR-II bioimaging, as well as the inspiration of new ideas in this field.

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 · 10.3389/fbioe.2019.00487

Cites

98 of 98 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

2 retracted

Show all 98 references

Cited by

See all 313

Metadata

Authors
Cao, J., Zhu, B., Zheng, K., He, S., Meng, L., Song, J., & Yang, H.
Venue
Frontiers in Bioengineering and Biotechnology
Published
2020-01-30
Volume
7
Pages
487-487
ISSN
2296-4185 (ISSN-L)
OpenAlex
W3003635276
Cited by
313
References
98
FWCI
19.72
Type
journal article
Language
en
Open access
gold

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: Recent Progress in NIR-II Contrast Agent for Biological Imaging. https://app.cochat.ai/papers/10.3389/fbioe.2019.00487
The paper
Cao, J., Zhu, B., Zheng, K., He, S., Meng, L., Song, J., & Yang, H. (2020). Recent Progress in NIR-II Contrast Agent for Biological Imaging. Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2019.00487

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.