System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Preprint Epublish

PubMed: 40654633 DOI: 10.1101/2025.05.01.651328

Data-driven feedback augments ultrasound nanotheranostics in brain tumors.

bioRxiv

Authors: Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J Kim, Tobey J MacDonald, Chetan Bettegowda, Graeme F Woodworth, Felix J Herrmann, Costas Arvanitis

Affiliations

  • Woodruf School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.
  • School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States.
  • Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.
  • Brain Tumor Program, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, Maryland, United States.
  • Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States.
  • Department of Bioengineering, University of Maryland, College Park, Maryland, United States.
  • Aflac Cancer & Blood Disorders Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.
  • Department of Neurosurgery, John Hopkins University School of Medicine, Baltimore, Maryland, United States.
  • School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States.

Abstract

The blood-brain barrier (BBB) renders the delivery of nanomedicine in the brain ineffective and the detection of circulating disease-related DNA from the brain unreliable. Here, we show that the acoustic emission content of focused ultrasound-controlled microbubble dynamics (MB-FUS) incorporates precursor signals that allow large-data models to predict sonication regimens for safe and effective BBB opening. Crucially, closed-loop MB-FUS controller augmented by machine learning (ML-CL) expands the treatment window (4-fold), as compared to conventional controllers, by persistently and proactively maximizing the BBB permeability while preventing tissue damage. By successfully scaling up from mice to rats and from healthy to diseased brains (glioma), ML-CL rendered the BBB permeable to large nanoparticles and markedly improved the release and detection of tumor DNA in plasma. Together, our findings reveal the potential of data-driven feedback to support the development of next-generation AI-powered ultrasound systems for safe, robust, and efficient nanotheranostic targeting of brain diseases.

MeSH

Brain Neoplasms, Feedback, Brain, Neoplasms