Evidence map›Paper›PMID 28974044›Full record

ArticleMaterials (Basel, Switzerland)2017

Magnetic Force-Driven Graphene Patterns to Direct Synaptogenesis of Human Neuronal Cells.

Kyung-Joon Min, Tae-Hyung Kim, Jeong-Woo Choi

Open access · goldAbstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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  4. Article
  5. 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 at 2 institutions in 1 country.

Kyung-Joon MinDepartment of Biomedical Engineering, Sogang University, 35 Baekbeom-ro (Sinsu-dong), Mapo-gu, Seoul 121-742, Korea. kjdmin@gmail.com.
Tae-Hyung KimSchool of Integrative Engineering, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea. thkim0512@cau.ac.kr.
Jeong-Woo ChoiDepartment of Biomedical Engineering, Sogang University, 35 Baekbeom-ro (Sinsu-dong), Mapo-gu, Seoul 121-742, Korea. jwchoi@sogang.ac.kr.
Sogang University · KRChung-Ang University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise control of axonal growth and synaptic junction formation are incredibly important to repair and/or to mimic human neuronal network. Here, we report a graphene oxide (GO)-based hybrid patterns that were proven to be excellent for guiding axonal growth and its consequent synapse formation of human neural cells. Unlike the previous method that utilized micro-contacting printing technique to generate GO patterns, here, GO-encapsulated magnetic nanoparticles were first synthesized and utilized as core materials wherein the external magnetic force facilitated the transfer of GO film to the desired substrate. Owing to the intrinsic property of GO that provides stable cell attachment and growth for long-term culture, human neuronal cells could be effectively patterned on the biocompatible polymer substrates with different pattern sizes. By using magnetic force-driven GO hybrid patterns, we demonstrated that accumulation and expression level of Synaptophysin of neurons could be effectively controlled with varying sizes of each pattern. The synaptic network between each neuron could be precisely controlled and matched by guiding axonal direction. This work provides treatment and modeling of brain diseases and spinal cord injuries.

Indexed as

axonal growth guidanceGO-encapsulated magnetic nanoparticlesGO hybrid patternhuman neuronal cellsynapse

Identifiers

PMID28974044
PMCPMC5666957
OpenAlexW2764104923

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.