ArticleToxics2024
Development of a Novel Microphysiological System for Peripheral Neurotoxicity Prediction Using Human iPSC-Derived Neurons with Morphological Deep Learning.
Article in Toxics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Organ-on-a-chip toxicology.Innovation (Cambridge (Mass.)) · 2026Review
- Engineering immune-competent hair follicle microphysiological systems: from organoid assembly to dynamic immune modelling.Frontiers in bioengineering and biotechnology · 2026Review
- Decoding chemotherapy-induced peripheral neuropathy: ion channels, disease models, and future directions.Frontiers in pharmacology · 2026Review
- An efficient dimensionality reduction framework using metaheuristic optimization with deep learning models for amyotrophic lateral sclerosis disease progression prediction.Scientific reports · 2025Article
- Smarter stem cells: how AI is supercharging iPSC technology.Cell and tissue research · 2025Review
- Evaluation of Perfusion Cell Culture Conditions in a Double-Layered Microphysiological System Using AI-Assisted Morphological Analysis.Micromachines · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A microphysiological system (MPS) is an in vitro culture technology that reproduces the physiological microenvironment and functionality of humans and is expected to be applied for drug screening. In this study, we developed an MPS for the structured culture of human iPSC-derived sensory neurons and then predicted drug-induced neurotoxicity by morphological deep learning. Using human iPSC-derived sensory neurons, after the administration of representative anti-cancer drugs, the toxic effects on soma and axons were evaluated by an AI model with neurite images. Significant toxicity was detected in positive drugs and could be classified by different effects on soma or axons, suggesting that the current method provides an effective evaluation of chemotherapy-induced peripheral neuropathy. The results of neurofilament light chain expression changes in the MPS device also agreed with clinical reports. Therefore, the present MPS combined with morphological deep learning is a useful platform for in vitro peripheral neurotoxicity assessment.
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Registered trials
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.