Evidence map›Paper›PMID 41517881›Full record

ReviewJournal of neurochemistry2026

Neurons in a Dish: A Review of In Vitro Cell Models for Studying Neurogenesis.

Mariana Vassal, Ana C Cruz, Sandra Rebelo, Filipa Martins

Abstract readReview
In one paragraph

Review in Journal of neurochemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Mariana VassalDepartment of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
Ana C CruzDepartment of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
Sandra RebeloDepartment of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
Filipa MartinsDepartment of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.ORCID https://orcid.org/0000-0002-3277-1809

Funding

Fundação para a Ciência e a Tecnologia 2023.01360.BDFundação para a Ciência e a Tecnologia 2024.03977.BDFundação para a Ciência e a Tecnologia PTDC/BTM-TEC/3792/2021Institute of Biomedicine, University of Aveiro UID 4501Ministério da Educação e Ciência
6 · The paper itself

Abstract

Understanding neurogenesis, the complex biological process of generating new neurons, is crucial for understanding brain development, function, and potential therapeutic interventions for neurological disorders. Due to the inherent difficulty of directly observing neurogenesis in the human brain, researchers heavily rely on cell models to simulate this process under controlled conditions. These models serve as invaluable tools to understand the mechanisms underlying the different stages of neurogenesis, helping researchers explore how neurons are generated, mature, and integrate into neural networks, thereby contributing to both normal brain function and neurological disorders. Therefore, this work provides a comprehensive overview of different cell models commonly used in neurogenesis research, from primary cultures and stem cells to immortalized cell lines. This compilation highlights the strengths and limitations of each cell model, which ultimately allows researchers to select the most appropriate model system for their research, thus enhancing the efforts towards unraveling the mysteries of the brain.

Indexed as

Neural Stem CellsNeurogenesisNeuronsAnimalsBrainCell DifferentiationHumansNeurodevelopmentcell modelsdifferentiationin vitroneurogenesisneurons

Identifiers

PMID41517881
PMCPMC12789853

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.