Evidence map›Paper›PMID 32681246›Full record

ReviewCurrent medical science2020

The Recent Advances of Fluorescent Sensors Based on Molecularly Imprinted Fluorescent Nanoparticles for Pharmaceutical Analysis.

Yi-Fan Wang, Meng-Meng Pan, Xu Yu, Li Xu

Abstract readReview
In one paragraph

Review in Current medical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. 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.

Yi-Fan WangSchool of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Meng-Meng PanSchool of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Xu YuSchool of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. xuyu@hust.edu.cn.
Li XuSchool of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. xuyu@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescent nanoparticles have good chemical stability and photostability, controllable optical properties and larger stokes shift. In light of their designability and functionability, the fluorescent nanoparticles are widely used as the fluorescent probes for diverse applications. To enhance the sensitivity and selectivity, the combination of the fluorescent nanoparticles with the molecularly imprinted polymer, i.e. molecularly imprinted fluorescent nanoparticles (MIFN), was an effective way. The sensor based on MIFN (the MIFN sensor) could be more compatible with the complex sample matrix, which was especially widely adopted in medical and biological analysis. In this mini-review, the construction method, detective mechanism and types of MIFN sensors are elaborated. The current applications of MIFN sensors in pharmaceutical analysis, including pesticides/herbicide, veterinary drugs/drugs residues and human related proteins, are highlighted based on the literature in the recent three years. Finally, the research prospect and development trend of the MIFN sensor are forecasted.

Indexed as

Biological AssayFluorescent DyesHumansMolecular ImprintingNanoparticlesPharmaceutical PreparationsSensitivity and SpecificityFluorescent DyesPharmaceutical Preparationsfluorescent nanoparticlesfluorescent sensormolecularly imprinted polymerpharmaceutical analysis

Identifiers

PMID32681246
PMCPMC7366466

What OpenQuestion holds

Textmetadata
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.