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. 2024 Aug 23;14(1):19595.
doi: 10.1038/s41598-024-70559-4.

Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure

Jovana Paunovic Pantic  1 , Danijela Vucevic  1 , Tatjana Radosavljevic  1 , Peter R Corridon  2   3   4   5 , Svetlana Valjarevic  6 , Jelena Cumic  7 , Ljubisa Bojic  8 , Igor Pantic  9   10   11   12
Affiliations

Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure

Jovana Paunovic Pantic et al. Sci Rep. .

Abstract

This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

Keywords: Gradient boosting; Machine learning; Nucleus; Random forest; Toxicology.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Comparison of hepatocyte nuclei belonging to the group treated with IONPs (panels A–D) with intact nuclei (panels E–H). While they appear morphologically similar, significant differences are observed in the quantifiers of the run-length matrix and discrete wavelet transform.
Fig. 2
Fig. 2
The average value of nuclear long run emphasis, short run emphasis, of run length nonuniformity and wavelet coefficient energies in hepatocyte ROIs belonging to the IONP-treated group and controls.
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) curve for the machine learning model based on gradient boosting classifier algorithm.
Fig. 4
Fig. 4
Receiver operating characteristic (ROC) curve for the machine learning model based on random forest classifier algorithm.
Fig. 5
Fig. 5
Confusion matrices for the Gradient Boosting and Random Forest models.
Fig. 6
Fig. 6
Classification report for the Gradient Boosting and Random Forest models.
See this image and copyright information in PMC

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References

    1. Dimitriadis, I., Zaninovic, N., Badiola, A. C. & Bormann, C. L. Artificial intelligence in the embryology laboratory: A review. Reprod. Biomed. Online10.1016/j.rbmo.2021.11.003 (2021). 10.1016/j.rbmo.2021.11.003 - DOI - PubMed
    1. Itto-Nakama, K. et al. Prediction of ethanol fermentation under stressed conditions using yeast morphological data. J. Biosci. Bioeng.135, 210–216. 10.1016/j.jbiosc.2022.12.008 (2023). 10.1016/j.jbiosc.2022.12.008 - DOI - PubMed
    1. Idowu, S. O. & Fatokun, A. A. Artificial intelligence (AI) to the rescue: Deploying machine learning to bridge the biorelevance gap in antioxidant assays. SLAS Technol.26, 16–25. 10.1177/2472630320962716 (2021). 10.1177/2472630320962716 - DOI - PMC - PubMed
    1. Kim, D., Min, Y., Oh, J. M. & Cho, Y. K. AI-powered transmitted light microscopy for functional analysis of live cells. Sci. Rep.9, 18428. 10.1038/s41598-019-54961-x (2019). 10.1038/s41598-019-54961-x - DOI - PMC - PubMed
    1. Corridon, P. R., Wang, X., Shakeel, A. & Chan, V. Digital technologies: Advancing individualized treatments through gene and cell therapies, pharmacogenetics, and disease detection and diagnostics. Biomedicines.10.3390/biomedicines10102445 (2022). 10.3390/biomedicines10102445 - DOI - PMC - PubMed

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