Evidence map›Paper›PMID 36538193›Full record

ArticleTissue engineering and regenerative medicine2023

Laser-Structured Si and PLGA Inhibit the Neuro2a Differentiation in Mono- and Co-Culture with Glia.

Despoina Angelaki, Paraskevi Kavatzikidou, Costas Fotakis, Emmanuel Stratakis, Anthi Ranella

Abstract read
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Article in Tissue engineering and regenerative medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

5 authors.

Despoina AngelakiInstitute of Electronic Structure and Laser, Foundation for Research and Technology- Hellas (IESL- FORTH), 711 10, Heraklion, Greece.
Paraskevi KavatzikidouInstitute of Electronic Structure and Laser, Foundation for Research and Technology- Hellas (IESL- FORTH), 711 10, Heraklion, Greece.
Costas FotakisInstitute of Electronic Structure and Laser, Foundation for Research and Technology- Hellas (IESL- FORTH), 711 10, Heraklion, Greece.
Emmanuel StratakisInstitute of Electronic Structure and Laser, Foundation for Research and Technology- Hellas (IESL- FORTH), 711 10, Heraklion, Greece. stratak@iesl.forth.gr.
Anthi RanellaInstitute of Electronic Structure and Laser, Foundation for Research and Technology- Hellas (IESL- FORTH), 711 10, Heraklion, Greece. ranthi@iesl.forth.gr.ORCID 0000-0002-5034-6267

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe first step towards a successful neural tissue engineering therapy is the development of an appropriate scaffold and the in vitro study of the cellular response onto it.

methodsHere, we fabricated nano- and micro- patterned Si surfaces via direct ultrafast laser irradiation, as well as their replicas in the biodegradable poly(lactide-co-glycolide), in order to use them as culture substrates for neuronal cells. The differentiation of neuro2a cells on the Si platforms and their replicas was studied both in a mono-culture and in a co-culture with glial cells (Schwann-SW10).

resultsIt was found that the substrate's roughness inhibits the differentiation of the neuronal cells even in the presence of the differentiation medium, and the higher the roughness is, the more the differentiation gets limited.

conclusionOur results highlight the importance of the substrate's topography for the controlled growth and differentiation of the neuronal cells and their further study via protein screening methods could shed light on the factors that lead to limited differentiation; thus, contributing to the long standing request for culture substrates that induce cells to differentiate.

Indexed as

NeurogliaTissue EngineeringCell DifferentiationCoculture TechniquesLasersGlia-neurons co-cultureLaser structuringNano/micro topographyNeural tissue engineeringNeuronal differentiation

Identifiers

PMID36538193
PMCPMC9852401

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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.