Evidence map›Paper›PMID 33298939›Full record

ArticleNature communications2020

Thermoplasmonic neural chip platform for in situ manipulation of neuronal connections in vitro.

Nari Hong, Yoonkey Nam

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Three-dimensional microelectrode arrays forBiomedical engineering letters · 2026
    Review
  2. Article
  3. Article
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  7. Review
  8. Article
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  10. Beyond a Transmission Cable-New Technologies to Reveal the Richness in Axonal Electrophysiology.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. An Overview of In Vitro Biological Neural Networks for Robot Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2023
    Review
  16. Article
  17. Neurons-on-a-Chip:Molecules and cells · 2022
    Review
  18. 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

2 authors.

Nari HongDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.ORCID 0000-0003-1273-9622
Yoonkey NamDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea. ynam@kaist.ac.kr.ORCID 0000-0003-4801-9547

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cultured neuronal networks with a controlled structure have been widely studied as an in vitro model system to investigate the relationship between network structure and function. However, most cell culture techniques lack the ability to control network structures during cell cultivation, making it difficult to assess functional changes induced by specific structural changes. In this study, we present an in situ manipulation platform based on gold-nanorod-mediated thermoplasmonics to interrogate an in vitro network model. We find that it is possible to induce new neurite outgrowths, eliminate interconnecting neurites, and estimate functional relationships in matured neuronal networks. This method is expected to be useful for studying functional dynamics of neural networks under controlled structural changes.

Indexed as

AnimalsCells, CulturedEmbryo, MammalianFeasibility StudiesGoldHippocampusHydrogelsMetal NanoparticlesNanotechnologyNanotubesNerve NetNeuritesPrimary Cell CultureRatsGoldHydrogels

Identifiers

PMID33298939
PMCPMC7726146

What OpenQuestion holds

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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.