Data-driven feedback augments ultrasound nanotheranostics in brain tumors Article Swipe
Hohyun Lee
,
Victor Menezes
,
Shiyan Zeng
,
Chulyong Kim
,
Cynthia M. Baseman
,
Jae Hyun Kim
,
Shalini Padmanabhan
,
Pranav Premdas
,
Naima Djeddar
,
Anton V. Bryksin
,
Nikhil Pandey
,
Pavlos Anastasiadis
,
Anthony J. Kim
,
Tobey J. MacDonald
,
Chetan Bettegowda
,
Graeme F. Woodworth
,
Felix J. Herrmann
,
Costas Arvanitis
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.01.651328
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.01.651328
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.05.01.651328
- https://www.biorxiv.org/content/biorxiv/early/2025/05/07/2025.05.01.651328.full.pdf
- OA Status
- green
- References
- 88
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410146464
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410146464Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.05.01.651328Digital Object Identifier
- Title
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Data-driven feedback augments ultrasound nanotheranostics in brain tumorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-07Full publication date if available
- Authors
-
Hohyun Lee, Victor Menezes, Shiyan Zeng, Chulyong Kim, Cynthia M. Baseman, Jae Hyun Kim, Shalini Padmanabhan, Pranav Premdas, Naima Djeddar, Anton V. Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J. Kim, Tobey J. MacDonald, Chetan Bettegowda, Graeme F. Woodworth, Felix J. Herrmann, Costas ArvanitisList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.05.01.651328Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/05/07/2025.05.01.651328.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/05/07/2025.05.01.651328.full.pdfDirect OA link when available
- Concepts
-
Ultrasound, Computer science, Psychology, Neuroscience, Medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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88Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 88 |
| abstract_inverted_index.By | 87 |
| abstract_inverted_index.as | 70 |
| abstract_inverted_index.by | 61, 75 |
| abstract_inverted_index.in | 10, 120 |
| abstract_inverted_index.of | 8, 17, 33, 117, 128, 135, 147 |
| abstract_inverted_index.to | 46, 72, 93, 98, 107, 131 |
| abstract_inverted_index.up | 90 |
| abstract_inverted_index.we | 26 |
| abstract_inverted_index.BBB | 54, 81, 105 |
| abstract_inverted_index.DNA | 20, 119 |
| abstract_inverted_index.The | 1 |
| abstract_inverted_index.and | 14, 52, 77, 95, 110, 115, 143 |
| abstract_inverted_index.for | 50, 140 |
| abstract_inverted_index.our | 123 |
| abstract_inverted_index.the | 6, 11, 15, 22, 29, 66, 80, 104, 113, 126, 133 |
| abstract_inverted_index.from | 21, 91, 96 |
| abstract_inverted_index.mice | 92 |
| abstract_inverted_index.rats | 94 |
| abstract_inverted_index.safe | 51 |
| abstract_inverted_index.show | 27 |
| abstract_inverted_index.that | 28, 42 |
| abstract_inverted_index.(BBB) | 4 |
| abstract_inverted_index.Here, | 25 |
| abstract_inverted_index.ML-CL | 102 |
| abstract_inverted_index.allow | 43 |
| abstract_inverted_index.brain | 12, 23, 148 |
| abstract_inverted_index.large | 108 |
| abstract_inverted_index.safe, | 141 |
| abstract_inverted_index.tumor | 118 |
| abstract_inverted_index.while | 83 |
| abstract_inverted_index.MB-FUS | 58 |
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| abstract_inverted_index.models | 45 |
| abstract_inverted_index.reveal | 125 |
| abstract_inverted_index.tissue | 85 |
| abstract_inverted_index.window | 68 |
| abstract_inverted_index.(ML-CL) | 64 |
| abstract_inverted_index.barrier | 3 |
| abstract_inverted_index.content | 32 |
| abstract_inverted_index.damage. | 86 |
| abstract_inverted_index.expands | 65 |
| abstract_inverted_index.focused | 34 |
| abstract_inverted_index.healthy | 97 |
| abstract_inverted_index.machine | 62 |
| abstract_inverted_index.plasma. | 121 |
| abstract_inverted_index.predict | 47 |
| abstract_inverted_index.release | 114 |
| abstract_inverted_index.renders | 5 |
| abstract_inverted_index.robust, | 142 |
| abstract_inverted_index.scaling | 89 |
| abstract_inverted_index.signals | 41 |
| abstract_inverted_index.support | 132 |
| abstract_inverted_index.systems | 139 |
| abstract_inverted_index.(MB-FUS) | 38 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.acoustic | 30 |
| abstract_inverted_index.compared | 71 |
| abstract_inverted_index.delivery | 7 |
| abstract_inverted_index.diseased | 99 |
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| abstract_inverted_index.findings | 124 |
| abstract_inverted_index.improved | 112 |
| abstract_inverted_index.learning | 63 |
| abstract_inverted_index.markedly | 111 |
| abstract_inverted_index.opening. | 55 |
| abstract_inverted_index.regimens | 49 |
| abstract_inverted_index.rendered | 103 |
| abstract_inverted_index.(4-fold), | 69 |
| abstract_inverted_index.(glioma), | 101 |
| abstract_inverted_index.Together, | 122 |
| abstract_inverted_index.augmented | 60 |
| abstract_inverted_index.detection | 16, 116 |
| abstract_inverted_index.diseases. | 149 |
| abstract_inverted_index.effective | 53 |
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| abstract_inverted_index.potential | 127 |
| abstract_inverted_index.precursor | 40 |
| abstract_inverted_index.targeting | 146 |
| abstract_inverted_index.treatment | 67 |
| abstract_inverted_index.AI-powered | 137 |
| abstract_inverted_index.Crucially, | 56 |
| abstract_inverted_index.controller | 59 |
| abstract_inverted_index.large-data | 44 |
| abstract_inverted_index.maximizing | 79 |
| abstract_inverted_index.preventing | 84 |
| abstract_inverted_index.sonication | 48 |
| abstract_inverted_index.ultrasound | 138 |
| abstract_inverted_index.blood-brain | 2 |
| abstract_inverted_index.circulating | 18 |
| abstract_inverted_index.closed-loop | 57 |
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| abstract_inverted_index.unreliable. | 24 |
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| abstract_inverted_index.permeability | 82 |
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| abstract_inverted_index.successfully | 88 |
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| abstract_inverted_index.disease-related | 19 |
| abstract_inverted_index.nanotheranostic | 145 |
| abstract_inverted_index.next-generation | 136 |
| abstract_inverted_index.ultrasound-controlled | 35 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile.value | 0.1677667 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |