Evidence map›Paper›PMID 37754070›Full record

ReviewBiosensors2023

Functional Nucleic Acid Probes Based on Two-Photon for Biosensing.

Kefeng Wu, Changbei Ma, Yisen Wang

Abstract readReview
In one paragraph

Review in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Kefeng WuGBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou 510700, China.
Changbei MaSchool of Life Sciences, Central South University, Changsha 410013, China.
Yisen WangGBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou 510700, China.

Funding

Key Research and Development Program of Guangdong Province 2019B090917007National Natural Science Foundation of China 61988102Science and Technology Planning Project of Guangdong Province 2019B090909011Wang Yisen E1Z1D10900; E3Z2D10300
6 · The paper itself

Abstract

Functional nucleic acid (FNA) probes have been widely used in environmental monitoring, food analysis, clinical diagnosis, and biological imaging because of their easy synthesis, functional modification, flexible design, and stable properties. However, most FNA probes are designed based on one-photon (OP) in the ultraviolet or visible regions, and the effectiveness of these OP-based FNA probes may be hindered by certain factors, such as their potential for photodamage and limited light tissue penetration. Two-photon (TP) is characterized by the nonlinear absorption of two relatively low-energy photons of near-infrared (NIR) light with the resulting emission of high-energy ultraviolet or visible light. TP-based FNA probes have excellent properties, including lower tissue self-absorption and autofluorescence, reduced photodamage and photobleaching, and higher spatial resolution, making them more advantageous than the conventional OP-based FNA probes in biomedical sensing. In this review, we summarize the recent advances of TP-excited and -activated FNA probes and detail their applications in biomolecular detection. In addition, we also share our views on the highlights and limitations of TP-based FNA probes. The ultimate goal is to provide design approaches for the development of high-performance TP-based FNA probes, thereby promoting their biological applications.

Indexed as

Diagnostic ImagingFluorescent DyesLightNucleic Acid ProbesPhotonsFluorescent DyesNucleic Acid ProbesbioimagingbiomoleculeDNA nanotechnologyfluorescent probelight-activatedRNAtwo-photon

Identifiers

PMID37754070
PMCPMC10527542

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.