Evidence map›Paper›PMID 42227594›Full record

ReviewBioconjugate chemistry2026

Near-Infrared and Shortwave Infrared Building Blocks for Activity-Based Sensors in Animals.

Tianmiao Wang, Xinyuan Li, Lei Guo, Shang Jia

Abstract readReview
In one paragraph

Review in Bioconjugate chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tianmiao WangDepartment of Chemistry and Biochemistry, Rutgers University─Newark, Newark, New Jersey 07102, United States.
Xinyuan LiDepartment of Chemistry and Biochemistry, Rutgers University─Newark, Newark, New Jersey 07102, United States.ORCID 0009-0005-0980-0092
Lei GuoDepartment of Civil Engineering, University of Arkansas, Fayetteville, Arkansas 72701, United States.ORCID 0000-0003-0013-3236
Shang JiaDepartment of Chemistry and Biochemistry, Rutgers University─Newark, Newark, New Jersey 07102, United States.ORCID 0000-0001-6285-8959

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activatable fluorescent probes operating in the near-infrared (NIR, 700-1000 nm) and shortwave-infrared (SWIR, also known as NIR-II, 1000-1700 nm) windows have become essential tools for bioimaging due to their deep tissue penetration, low background interference, and high signal-to-noise ratios. With growing numbers of such probes in the red-shifted windows developed for detection in animal models, this review summarizes how and what NIR and SWIR fluorophores can be coupled with selective triggers to create activity-based sensing NIR/NIR-II probes rather than listing probes based on their bioanalytes. To date, a variety of probes have been constructed this way, involving the modular combination of a fluorophore scaffold that defines photophysical properties, defined modification sites where caging groups are installed, and connection chemistry that links responsive moieties to the fluorophore core. Through representative examples, we illustrate how the same fluorophore can be adapted for diverse applications by varying the caging group, and how the same responsive chemistry can be implemented across different scaffolds by preserving the linkage type. This modular framework provides researchers with a practical design toolkit for constructing activatable probes for new biological targets.

Indexed as

Fluorescent DyesInfrared RaysAnimalsFluorescent Chemosensor CompoundsOptical ImagingSpectroscopy, Near-InfraredFluorescent Chemosensor CompoundsFluorescent Dyes

Identifiers

PMID42227594
PMCPMC13281388

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.