Evidence map›Paper›PMID 34141699›Full record

ReviewFrontiers in chemistry2021

The Application of Nucleic Acid Probe-Based Fluorescent Sensing and Imaging in Cancer Diagnosis and Therapy.

Ge Huang, Chen Su, Lijuan Wang, Yanxia Fei, Jinfeng Yang

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

9 citing papers in PubMed, 23 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Ge HuangDepartment of Medicine, University of South China, Hengyang, China.
Chen SuDepartment of Anesthesiology and Pain Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha, China.
Lijuan WangDepartment of Medicine, University of South China, Hengyang, China.
Yanxia FeiDepartment of Anesthesiology and Pain Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha, China.
Jinfeng YangDepartment of Anesthesiology and Pain Medicine, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha, China.
Hunan Cancer Hospital · CNUniversity of South China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well known that cancer incidence and death rates have been growing, but the development of cancer theranostics and therapeutics has been a challenging work. Recently, nucleic acid probe-based fluorescent sensing and imaging have achieved remarkable improvements in a variety of cancer management techniques, credited to their high sensitivity, good tolerance to interference, fast detection, and high versatility. Herein, nucleic acid probe-based fluorescent sensing and imaging are labeled with advanced fluorophores, which are essential for fast and sensitive detection of aberrant nucleic acids and other cancer-relevant molecules, consequently performing cancer early diagnosis and targeted treatment. In this review, we introduce the characteristics of nucleic acid probes, summarize the development of nucleic acid probe-based fluorescent sensing and imaging, and prominently elaborate their applications in cancer diagnosis and treatment. In discussion, some challenges and perspectives are elaborated in the field of nucleic acid probe-based fluorescent sensing and imaging.

Indexed as

cancer diagnosiscancer therapyfluorescent imagingfluorescent sensingnucleic acid probes

Identifiers

PMID34141699
PMCPMC8204288
OpenAlexW3170006914

What OpenQuestion 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 OpenQuestion graph.