Evidence map›Paper›PMID 39634567›Full record

ArticleiScience2024

Evaluating chemical effects on human neural cells through calcium imaging and deep learning.

Ray Yueh Ku, Ankush Bansal, Dipankar J Dutta, Satoshi Yamashita, John Peloquin, Diana N Vu, Yubing Shen, Tomoki Uchida, Masaaki Torii, Kazue Hashimoto-Torii

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Ray Yueh KuCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Ankush BansalCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Dipankar J DuttaCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Satoshi YamashitaCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
John PeloquinCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Diana N VuCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Yubing ShenCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Tomoki UchidaNovel Business Development Department, Suntory Global Innovation Center Limited, 8-1-1 Seikadai, Seika-cho, Soraku-gun, Kyoto 619-0284, Japan.
Masaaki ToriiCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.
Kazue Hashimoto-ToriiCenter for Neuroscience Research, Children's Research Institute, Children's National Hospital, Washington, DC 20010, USA.

Funding

Neurobehavioral Evaluation CoreP50HD105328 · NICHD · CHILDREN'S RESEARCH INSTITUTE · PI WILLIAM Davis GAILLARD · 2021 to 2026
$9.3M
Mechanisms and treatments of learning deficits in Fetal Alcohol Spectrum DisordersR01AA026272 · NIAAA · CHILDREN'S RESEARCH INSTITUTE · PI HASHIMOTO-TORII, KAZUE, TORII, MASAAKI · 2019 to 2023
$2.4M
Investigating the effect of alcohol exposure on human cortical development using a 3D in vitro modelF32AA028163 · NIAAA · CHILDREN'S RESEARCH INSTITUTE · PI KU, RAY YUEH · 2019 to 2023
$214k
NIAAA NIH HHS F32 AA028163NIAAA NIH HHS R01 AA026272NICHD NIH HHS P50 HD105328
6 · The paper itself

Abstract

New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of the species-specific characteristics of human neural cells and advancements in machine learning technology, the development of effective and efficient methods for the initial screening of chemical effects on human neural function using machine learning platforms is anticipated. In this study, we employed a deep learning model to analyze calcium dynamics in human-induced pluripotent stem cell-derived neural progenitor cells, which were exposed to various concentrations of four representative chemicals. We report that this approach offers a reliable and concise method for quantitatively classifying the effects of chemical exposures and predicting potential harm to human neural cells.

Indexed as

Biological sciencesMachine learningNeuroscience

Identifiers

PMID39634567
PMCPMC11616611

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.