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    Area of Science:

    • Neuroscience
    • Computational Biology
    • Pharmacology

    Background:

    • Hypoxic-ischemic encephalopathy (HIE) is a critical condition affecting newborns, with therapeutic hypothermia (TH) as the sole validated treatment.
    • Current drug development for HIE is hampered by inefficient manual cell counting and analysis methods.
    • There is a significant need for automated, accurate quantification of neuronal cells in HIE research.

    Purpose of the Study:

    • To develop and validate a two-step automated cell analysis pipeline for segmenting and classifying neurons in fetal sheep brains.
    • To enable rapid and accurate assessment of neuronal damage in pre-clinical models of HIE.
    • To provide a tool for accelerating drug discovery and development for HIE.

    Main Methods:

    • A Mask R-CNN model was trained on a dataset of 44,000 annotated cells from 6 brain regions in fetal sheep, achieving 88.3% average precision for neuron segmentation.
    • A custom Convolutional Neural Network (CNN) was developed to classify cell morphology (healthy, intermediate, pyknotic), reaching 93.0% accuracy with zero misclassification of healthy cells.
    • The pipeline integrates segmentation and classification to quantify neuronal health indicators.

    Main Results:

    • The automated pipeline accurately quantifies neuronal cell populations and their morphology.
    • The pipeline's healthy cell counts strongly correlate with manual quantifications across different treatment groups (Spearman's R=0.822-0.919).
    • The system provides novel indicators of HIE-induced neuronal damage based on the proportion of healthy neurons.

    Conclusions:

    • The developed automated pipeline offers a robust and efficient method for analyzing neuronal damage in HIE research.
    • This tool can significantly accelerate preclinical drug development for HIE by reducing labor, time, and observer bias.
    • The pipeline enables a more detailed and reliable evaluation of therapeutic interventions in HIE models.