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Record W4391233082 · doi:10.1016/j.jconrel.2024.01.034

Focused ultrasound-assisted delivery of immunomodulating agents in brain cancer

2024· review· en· W4391233082 on OpenAlexafffund
Elahe Memari, Dure Khan, Ryan Alkins, Brandon Helfield

Bibliographic record

VenueJournal of Controlled Release · 2024
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsKingston Health Sciences CentreQueen's UniversityConcordia University
FundersCanadian Aeronautics and Space InstituteCancer Research SocietyNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsBurroughs Wellcome Fund
KeywordsMedicineImmunotherapyBrain cancerContext (archaeology)CancerBrain tumorBlood–brain barrierMicrobubblesImmune systemCancer immunotherapyImmunologyPathologyUltrasoundCentral nervous systemInternal medicineBiology

Abstract

fetched live from OpenAlex

Focused ultrasound (FUS) combined with intravascularly circulating microbubbles can transiently increase the permeability of the blood-brain barrier (BBB) to enable targeted therapeutic delivery to the brain, the clinical testing of which is currently underway in both adult and pediatric patients. Aside from traditional cancer drugs, this technique is being extended to promote the delivery of immunomodulating therapeutics to the brain, including antibodies, immune cells, and cytokines. In this manner, FUS approaches are being explored as a tool to improve and amplify the effectiveness of immunotherapy for both primary and metastatic brain cancer, a particularly challenging solid tumor to treat. Here, we present an overview of the latest groundbreaking research in FUS-assisted delivery of immunomodulating agents to the brain in pre-clinical models of brain cancer, and place it within the context of the current immunotherapy approaches. We follow this up with a discussion on new developments and emerging strategies for this rapidly evolving approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.315
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2024
Admission routes2
Has abstractyes

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