Evidence map›Paper›PMID 41498583›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

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

Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Hohyun LeeWoodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0002-7939-6751
Victor MenezesWoodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0009-0009-8377-0054
Shiqin ZengSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0002-9798-5576
Chulyong KimWoodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0009-0000-7550-0980
Cynthia M BasemanSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0009-0004-8507-438X
Jae Hyun KimWoodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0009-0004-9603-9002
Samhita PadmanabhanCoulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0003-0780-7248
Pranav PremdasSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0002-8451-0467
Naima DjeddarInstitute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0009-0006-9131-7370
Anton BryksinInstitute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0003-3302-8248
Nikhil PandeyDepartment of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-6560-6893
Pavlos AnastasiadisDepartment of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-0230-7925
Anthony J KimDepartment of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0002-8385-417X
Tobey J MacDonaldAflac Cancer & Blood Disorders Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0003-4918-1300
Chetan BettegowdaDepartment of Neurosurgery, John Hopkins University School of Medicine, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0001-9991-7123
Graeme F WoodworthDepartment of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.ORCID https://orcid.org/0000-0001-9663-0768
Felix J HerrmannSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0003-1180-2167
Costas ArvanitisWoodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.ORCID https://orcid.org/0000-0002-7737-1446

Funding

"Transcranial FUS therapy with closed-loop US image guidance and circulating tumor DNAR37CA239039 · NCI · GEORGIA INSTITUTE OF TECHNOLOGY · PI Konstantinos-Costas Arvanitis · 2020 to 2026
$3.3M
Focused Ultrasound Foundation AWD 004091NIH HHS R37CA239039
6 · The paper itself

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.

Indexed as

Blood-Brain BarrierBrainBrain NeoplasmsDrug Delivery SystemsGliomaTheranostic NanomedicineAnimalsHumansMachine LearningMiceMicrobubblesNanoparticlesRatsSonicationblood‐brain barrierbrain cancerfocused ultrasoundliquid biopsymachine learningnanoparticletheranostics

Identifiers

PMID41498583
PMCPMC12955872

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.