Evidence map›Paper›PMID 39914783›Full record

ArticleEuropean journal of pharmacology2025

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Christopher Michael McGraw, Annapurna Poduri

Abstract read
In one paragraph

Article in European journal of pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Christopher Michael McGrawDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, USA; Department of Neurology, The F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, USA. Electronic address: christopher.mcgraw@northwestern.edu.
Annapurna PoduriDepartment of Neurology, The F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA; National Institutes of Health (NIH), Bethesda, MD, USA.

Funding

Developing a Zebrafish Model of Slc6a1/GAT1 Hypofunction and an In Vitro Assay to Identify Novel TreatmentsK08NS118107 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI MCGRAW, CHRISTOPHER · 2020 to 2024
$1.1M
NINDS NIH HHS K08 NS118107NINDS NIH HHS L40 NS124071
6 · The paper itself

Abstract

Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited by incomplete detection of anti-seizure responses (locomotor assays) or low-throughput (electrophysiology, fluorescence microscopy). To overcome these challenges, we developed a novel high-throughput method using combined locomotor and calcium fluorescence data from unrestrained larval zebrafish in a 96-well plate reader. Custom software tracked fish movement and fluorescence changes (deltaF/F0) from high-speed time-series, and logistic classifiers trained with elastic net regression distinguished seizure-like activity in response to the GABA

Indexed as

AnticonvulsantsCalciumHigh-Throughput Screening AssaysMachine LearningSeizuresZebrafishAnimalsDrug Evaluation, PreclinicalFluorescenceLarvaMovementPentylenetetrazoleValproic AcidAnticonvulsantsCalciumPentylenetetrazoleValproic AcidCalcium fluorescenceHigh-throughput imagingPro-convulsantsPTZSeizuresZebrafish

Identifiers

PMID39914783
PMCPMC12303219

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Registered trials

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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.