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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.

Radiodynamic therapy (RDT) is an emerging non-invasive anti-cancer treatment based on the generation of the reactive oxygen species (ROS) at the lesion site following the interaction between X-rays and a photosensitizer drug (PS). The broader application of RDT is impeded by the tumor-associated hypoxia that results in low availability of oxygen for the generation of sufficient amounts of ROS. Herein, a novel nanoparticle drug formulation for RDT, which addresses the problem of low oxygen availability, is reported. It consists of poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) co-loaded with a PS drug verteporfin (VP), and the clinically approved oxygen-carrying molecule, perfluorooctylbromide (PFOB). When triggered by X-rays (4 Gy), under both normoxic and hypoxic conditions, PLGA-VP-PFOB nanoconstructs (NCs) induced a significant increase of the ROS production compared with matching PLGA-VP nanoparticles. The RDT with NCs effectively killed ~60% of human pancreatic cancer cells in monolayer cultures, and almost completely suppressed the outgrowth of tumor cells in 2-weeks clonogenic assay. In a 3D engineered model of pancreatic cancer metastasis to the liver, RDT with NCs destroyed ~35% of tumor cells, demonstrating an exceptional efficiency at a tissue level. These results show that PLGA-VP-PFOB is a promising agent for RDT of deep-seated hypoxic tumors.