Evidence map›Paper›PMID 35054758›Full record

ReviewPolymers2022

Polymer-Based Nanofiber-Nanoparticle Hybrids and Their Medical Applications.

Mingxin Zhang, Wenliang Song, Yunxin Tang, Xizi Xu, Yingning Huang, Dengguang Yu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

  1. Review
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  9. Graphene Oxide: Preparation and Medical Research.Materials (Basel, Switzerland) · 2025
    Review
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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

6 authors.

Mingxin ZhangSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.ORCID 0000-0002-7255-2600
Wenliang SongSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yunxin TangSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.
Xizi XuSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yingning HuangSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.
Dengguang YuSchool of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China.ORCID 0000-0001-7825-4498

Funding

The Natural Science Foundation of Shanghai No. 20ZR1439000
6 · The paper itself

Abstract

The search for higher-quality nanomaterials for medicinal applications continues. There are similarities between electrospun fibers and natural tissues. This property has enabled electrospun fibers to make significant progress in medical applications. However, electrospun fibers are limited to tissue scaffolding applications. When nanoparticles and nanofibers are combined, the composite material can perform more functions, such as photothermal, magnetic response, biosensing, antibacterial, drug delivery and biosensing. To prepare nanofiber and nanoparticle hybrids (NNHs), there are two primary ways. The electrospinning technology was used to produce NNHs in a single step. An alternate way is to use a self-assembly technique to create nanoparticles in fibers. This paper describes the creation of NNHs from routinely used biocompatible polymer composites. Single-step procedures and self-assembly methodologies are used to discuss the preparation of NNHs. It combines recent research discoveries to focus on the application of NNHs in drug release, antibacterial, and tissue engineering in the last two years.

Indexed as

electrospinningmedical applicationsnanoparticlepolymer blendspolymer composites

Identifiers

PMID35054758
PMCPMC8780324

What OpenQuestion holds

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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.