Unique Deep Radiomic Signature Shows NMN Treatment Reverses Morphology of Oocytes from Aged Mice

The purpose of this study is to develop a deep radiomic signature based on an artificial intelligence (AI) model. This radiomic signature identifies oocyte morphological changes corresponding to reproductive aging in bright field images captured by optical light microscopy. Oocytes were collected fr...

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Bibliographic Details
Main Authors: Bertoldo, M.J (Author), Campbell, J.M (Author), Gilchrist, R.B (Author), Goldys, E.M (Author), Goss, D.M (Author), Habibalahi, A. (Author), Ledger, W.L (Author), Mahbub, S.B (Author), Wu, L.E (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
NMN
Online Access:View Fulltext in Publisher
LEADER 02676nam a2200289Ia 4500
001 10.3390-biomedicines10071544
008 220718s2022 CNT 000 0 und d
020 |a 22279059 (ISSN) 
245 1 0 |a Unique Deep Radiomic Signature Shows NMN Treatment Reverses Morphology of Oocytes from Aged Mice 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/biomedicines10071544 
520 3 |a The purpose of this study is to develop a deep radiomic signature based on an artificial intelligence (AI) model. This radiomic signature identifies oocyte morphological changes corresponding to reproductive aging in bright field images captured by optical light microscopy. Oocytes were collected from three mice groups: young (4-to 5-week-old) C57BL/6J female mice, aged (12-month-old) mice, and aged mice treated with the NAD+ precursor nicotinamide mononucleotide (NMN), a treatment recently shown to rejuvenate aspects of fertility in aged mice. We applied deep learning, swarm intelligence, and discriminative analysis to images of mouse oocytes taken by bright field microscopy to identify a highly informative deep radiomic signature (DRS) of oocyte morphology. Predictive DRS accuracy was determined by evaluating sensitivity, specificity, and cross-validation, and was visualized using scatter plots of the data associated with three groups: Young, old and Old + NMN. DRS could successfully distinguish morphological changes in oocytes associated with maternal age with 92% accuracy (AUC~1), reflecting this decline in oocyte quality. We then employed the DRS to evaluate the impact of the treatment of reproductively aged mice with NMN. The DRS signature classified 60% of oocytes from NMN-treated aged mice as having a ‘young’ morphology. In conclusion, the DRS signature developed in this study was successfully able to detect aging-related oocyte morphological changes. The significance of our approach is that DRS applied to bright field oocyte images will allow us to distinguish and select oocytes originally affected by reproductive aging and whose quality has been successfully restored by the NMN therapy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a aging 
650 0 4 |a machine learning 
650 0 4 |a morphology 
650 0 4 |a NMN 
650 0 4 |a oocyte 
700 1 |a Bertoldo, M.J.  |e author 
700 1 |a Campbell, J.M.  |e author 
700 1 |a Gilchrist, R.B.  |e author 
700 1 |a Goldys, E.M.  |e author 
700 1 |a Goss, D.M.  |e author 
700 1 |a Habibalahi, A.  |e author 
700 1 |a Ledger, W.L.  |e author 
700 1 |a Mahbub, S.B.  |e author 
700 1 |a Wu, L.E.  |e author 
773 |t Biomedicines