AIMC Topic: Embryo, Nonmammalian
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Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.
Nature communications
Jul 16, 2025
Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of tradition...
Model-driven high-throughput zebrafish embryo assay for evaluating whole effluent toxicity variation across 100 full-scale wastewater treatment plants.
Water research
Apr 17, 2025
The zebrafish embryo is a valuable model for evaluating whole effluent toxicity (WET). However, the widely recognized acute toxicity indicator, based on International Organization of Standardization (ISO) methods, requires large numbers of embryos an...
Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity.
Journal of hazardous materials
Jan 20, 2025
Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays....
The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.
Developmental cell
Jan 15, 2025
The 5' UTRs of mRNAs are critical for translation regulation during development, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massivel...
Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.
Journal of hazardous materials
Oct 29, 2024
Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new...
Prediction of developmental toxic effects of fine particulate matter (PM) water-soluble components via machine learning through observation of PM from diverse urban areas.
The Science of the total environment
Jun 19, 2024
The global health implications of fine particulate matter (PM) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM from two cities (Har...
Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d.
BioTechniques
Feb 29, 2024
In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA knockdown and Cas9-mediated DNA targeting in zebrafish embryos. They targeted the no tail () gene as...
Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.
Nature
Dec 12, 2023
Enhancers control gene expression and have crucial roles in development and homeostasis. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning t...
Deep Learning-Enabled Morphometric Analysis for Toxicity Screening Using Zebrafish Larvae.
Environmental science & technology
Mar 27, 2023
Toxicology studies heavily rely on morphometric analysis to detect abnormalities and diagnose disease processes. The emergence of ever-increasing varieties of environmental pollutants makes it difficult to perform timely assessments, especially using...
A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.
Scientific reports
Jan 10, 2022
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell memb...
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