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Record W1912665088 · doi:10.1186/2050-5736-3-s1-p8

Quantitative evaluation of blood-tumor-barrier response following focused ultrasound and microbubble treatment in rat glioma: can we improve drug delivery to brain tumors?

2015· article· en· W1912665088 on OpenAlexaff
Hassaan Ahmed

Bibliographic record

VenueJournal of Therapeutic Ultrasound · 2015
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineGliomaUltrasoundDrug deliveryDrugFocused ultrasoundBrain tumorBlood–brain barrierPathologyRadiologyCancer researchPharmacologyInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

The increased interstitial fluid pressure (IFP) from vasogenic edema, which results from a leaky blood-tumor-barrier (BTB), creates a barrier for transvascular drug delivery, particularly larger molecules that are unable to diffuse across and must resort to bulk fluid flow. We have previously used dynamic contrast enhanced-computed tomography (DCE-CT) to quantitatively evaluate the blood-brain-barrier (BBB) response in normal brain - and have shown that focused ultrasound (FUS) and microbubble (MB) treatment in a rat brain demonstrates a 3-4 times transient increase at our optimized parameters. The purpose of this study was to quantitatively evaluate the BTB response following FUS and MB treatment in rat glioma, in the context of improving drug delivery to brain tumors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2015
Admission routes1
Has abstractyes

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