Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

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 demonstrate that microbubble-enhanced focused ultrasound (MB-FUS) mediated BBB opening, supported by large-data models predict sonication regimens for safe and effective BBB opening. Importantly, a closed-loop MB-FUS controller augmented by machine learning (ML-CL) expands the treatment window, 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 reporter gene DNA from tumors in blood. 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 and treatment of brain diseases.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2026-01-07
DOI
https://doi.org/10.1002/advs.202517834
Primary Topic
Ultrasound and Hyperthermia Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Felix J. Herrmann, Costas Arvanitis, Anton V. Bryksin, Nidhi Pandey et al.
Advanced Science
Ultrasound and Hyperthermia Applications
article

Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Felix J. Herrmann, Costas Arvanitis, Anton V. Bryksin, Nidhi Pandey, Chulyong Kim, Pavlos Anastasiadis, T. MacDonald, Naima Djeddar, Hohyun Lee, Chetan Bettegowda, Cynthia M. Baseman, Jae Hyun Kim, Victor Menezes, Pranav Premdas, Shiqin Zeng, Graeme F. Woodworth, Anthony J. Kim, Samhita Padmanabhan
article en

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 demonstrate that microbubble-enhanced focused ultrasound (MB-FUS) mediated BBB opening, supported by large-data models predict sonication regimens for safe and effective BBB opening. Importantly, a closed-loop MB-FUS controller augmented by machine learning (ML-CL) expands the treatment window, 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 reporter gene DNA from tumors in blood. 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 and treatment of brain diseases.

Advanced ScienceVol. 13(13)
University of Maryland, Baltimore (US), Georgia Institute of Technology (US), Johns Hopkins University (US), Emory University (US), Johns Hopkins Medicine (US), The Wallace H. Coulter Department of Biomedical Engineering (US), Woodruff Health Sciences Center (US), Children's Healthcare of Atlanta (US), University of Baltimore (US)
Focused Ultrasound Foundation, National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 96%
Ultrasound and Hyperthermia Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.