Evidence map›Paper›PMID 42038106›Full record

ReviewFrontiers in toxicology2026

Towards learning and memory risk assessment with human brain organoids: barriers and opportunities.

Ronit Mohapatra, Dowlette-Mary Alam El Din, Hanyu Zhao, Thomas Hartung, Lena Smirnova

Abstract readReview
In one paragraph

Review in Frontiers in toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Ronit MohapatraDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Dowlette-Mary Alam El DinDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Hanyu ZhaoDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Thomas HartungDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.
Lena SmirnovaDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Johns Hopkins University, Baltimore, MD, United States.

Funding

Traning Program in Environmental Health SciencesT32ES007141 · NIEHS · JOHNS HOPKINS UNIVERSITY · PI Marsha Wills-Karp · 1985 to 2026
$22.2M
NIEHS NIH HHS T32 ES007141
6 · The paper itself

Abstract

Neurodevelopmental conditions, including autism spectrum disorder, intellectual disability, and learning disabilities, as well as neurodegenerative disorders, affect millions of people in the United States alone. Both genetic and environmental factors contribute to their onset, yet traditional neurotoxicity testing often fails to identify specific risks or mechanisms underlying cognitive impairment. Human brain organoids (hBOs), also called neural organoids or brain microphysiological systems, are three-dimensional (3D) stem cell-derived models that recapitulate key features of human brain development and offer greater physiological relevance than traditional 2D

Indexed as

brain organoidsheavy metalslearningmemoryneurotoxicitynew approach methodologies (NAMs)organoid intelligence

Identifiers

PMID42038106
PMCPMC13105461

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.