Leveraging Real-Time Big Data Analytics and Reinforcement Learning to Optimize Stem Cell Graft Engraftment and Neuroplasticity in Pediatric Autism Models

• Abilly Elly

  Texas University

  Author

Autism Spectrum Disorder (ASD) presents a complex neurodevelopmental challenge characterized by impaired synaptic connectivity, neuroinflammation, and gut-brain axis dysregulation, affecting approximately 1 in 36 children in the United States. Current stem cell transplantation approaches, while promising, suffer from passive graft integration, uncontrolled synaptic formation, and inability to dynamically adapt to patient-specific neural microenvironments. This research proposes a closed-loop adaptive neural regeneration framework integrating real-time big data analytics with reinforcement learning (RL) to optimize stem cell graft engraftment and neuroplasticity in pediatric ASD models. The system architecture combines a Deep Neural Network (DNN) prediction module achieving 96.98% accuracy in ASD trait identification, a Deep Deterministic Policy Gradient (DDPG) reinforcement learning agent for personalized intervention optimization, and a closed-loop feedback mechanism incorporating calcium imaging and electrophysiological readouts to dynamically adjust stimulation parameters. Experimental validation using the ABIDE I and II neuroimaging datasets, combined with simulated stem cell graft integration data, demonstrated that the closed-loop RL framework achieved an 89.4% engraftment optimization rate, surpassing static intervention protocols by 23.7%. The system successfully identified key predictors of graft success, including Qchat-10-Score, microbial diversity indices (Shannon index), and hippocampal neuroinflammation markers (IL-6, TNF-α) . Practical implications include a scalable, data-driven approach for personalized ASD intervention that reduces high-risk cases from 65% to 25% in simulated cohorts. This framework establishes a replicable paradigm for integrating artificial intelligence with regenerative medicine, addressing critical gaps in current stem cell therapeutic strategies.

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Copyright (c) 2026 Abilly Elly (Author)

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