Evidence map›Paper›PMID 31555074›Full record

ArticleFrontiers in neuroscience2019

Functional Scaffolding for Brain Implants: Engineered Neuronal Network by Microfabrication and iPSC Technology.

Kenta Shimba, Chih-Hsiang Chang, Takahiro Asahina, Fumika Moriya, Kiyoshi Kotani, Yasuhiko Jimbo, Arseniy Gladkov, Oksana Antipova, Yana Pigareva, Vladimir Kolpakov and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2019. 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
0.9field-weighted citation impact, top 31% 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

8 citing papers in PubMed, 13 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

13 authors at 3 institutions in 2 countries.

Kenta ShimbaDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Chih-Hsiang ChangDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Takahiro AsahinaDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Fumika MoriyaDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Kiyoshi KotaniDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Yasuhiko JimboDepartment of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Arseniy GladkovDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Oksana AntipovaDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Yana PigarevaDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Vladimir KolpakovDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Irina MukhinaDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Victor KazantsevDepartment of Neurotechnology, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Alexey PimashkinDepartment of Neuroengineering, Center of Translational Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
N. I. Lobachevsky State University of Nizhny Novgorod · RUThe University of Tokyo · JPPrivolzhsky Research Medical University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroengineering methods can be effectively used in the design of new approaches to treat central nervous system and brain injury caused by neurotrauma, ischemia, or neurodegenerative disorders. During the last decade, significant results were achieved in the field of implant (scaffold) development using various biocompatible and biodegradable materials carrying neuronal cells for implantation into the injury site of the brain to repair its function. Neurons derived from animal or human induced pluripotent stem (iPS) cells are expected to be an ideal cell source, and induction methods for specific cell types have been actively studied to improve efficacy and specificity. A critical goal of neuro-regeneration is structural and functional restoration of the injury site. The target treatment area has heterogeneous and complex network topology with various types of cells that need to be restored with similar neuronal network structure to recover correct functionality. However, current scaffold-based technology for brain implants operates with homogeneous neuronal cell distribution, which limits recovery in the damaged area of the brain and prevents a return to fully functional biological tissue. In this study, we present a neuroengineering concept for designing a neural circuit with a pre-defined unidirectional network architecture that provides a balance of excitation/inhibition in the scaffold to form tissue similar to that in the injured area using various types of iPS cells. Such tissue will mimic the surrounding niche in the injured site and will morphologically and topologically integrate into the brain, recovering lost function.

Indexed as

3D scaffold brain implanthuman induced pluripotent stem cellmicroelectrode arraymicrofabricationmicrofluidicsneural tissue engineeringneuronal network

Identifiers

PMID31555074
PMCPMC6727854
OpenAlexW2968059887

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.