Evidence mapPaperPMID 41470537Full record

ReviewMicromachines2025

Advances in NIR-II Fluorescent Nanoprobes: Design Principles, Optical Engineering, and Emerging Translational Directions.

Nargish Parvin, Mohammad Aslam, Md Najib Alam, Tapas K Mandal

Abstract readReview
In one paragraph

Review in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Nargish ParvinSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0002-1209-1507
Mohammad AslamSchool of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0002-0574-5621
Md Najib AlamSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0001-7600-6183
Tapas K MandalSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0003-2615-8618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescent nanoprobes operating in the NIR-II window have gained considerable attention for biomedical imaging because of their deep-tissue penetration, reduced scattering, and high spatial resolution. Their tunable optical behavior, flexible surface chemistry, and capacity for multifunctional design enable sensitive detection and targeted visualization of biological structures in vivo. This review highlights recent advances in the design and optical engineering of four widely studied NIR-II nanoprobe families: quantum dots, carbon dots, upconversion nanoparticles, and dye-doped silica nanoparticles. These materials were selected because they offer well-defined architectures, controllable emission properties, and substantial mechanistic insight supporting discussions of imaging performance and translational potential. Particular focus is placed on emerging strategies for activatable, targeted, and ratiometric probe construction. Recent efforts addressing biosafety, large-scale synthesis, optical stability, and early preclinical validation are also summarized to clarify the current progress and remaining challenges that influence clinical readiness. By outlining these developments, this review provides an updated and focused perspective on how engineered NIR-II nanoprobes are advancing toward practical use in biomedical imaging and precision diagnostics.

Indexed as

biomedical imagingdisease diagnosisfluorescent nanoprobesin vivo biosensingquantum dots

Identifiers

PMID41470537
PMCPMC12734865

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.