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Record W2887060037 · doi:10.1038/s41598-018-30825-8

Angiogenic response of rat hippocampal vasculature to focused ultrasound-mediated increases in blood-brain barrier permeability

2018· article· en· W2887060037 on OpenAlexaff
Dallan McMahon, Ethan Mah, Kullervo Hynynen

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsMicrobubblesHippocampal formationBlood–brain barrierVascular permeabilityMedicineHippocampusBlood vesselNeurosciencePathologyInternal medicineUltrasoundBiologyCentral nervous system

Abstract

fetched live from OpenAlex

Focused ultrasound (FUS) and circulating microbubbles can induce a targeted and transient increase in blood-brain barrier permeability. While preclinical research has demonstrated the utility of FUS for efficacious drug deliver to the brain, there remain gaps in our knowledge regarding the long-term response of brain vasculature to this intervention. Previous work has demonstrated transcriptional changes in hippocampal microvessels following sonication that are indicative of the initiation of angiogenic processes. Moreover, blood vessel growth has been reported in skeletal muscle following application of FUS and microbubbles. The current study demonstrates that blood vessel density in the rat hippocampus is modestly elevated at 7 and 14 d post-FUS compared to the contralateral hemisphere (7 d: 10.9 ± 6.0%, p = 0.02; 14 d: 12.1 ± 3.2%, p < 0.01), but returns to baseline by 21 d (5.9 ± 2.6%, p = 0.12). Concurrently, relative newborn endothelial cell density and frequency of small blood vessel segments were both elevated in the sonicated hippocampus. While further work is required to determine the mechanisms driving these changes, the findings presented here may have relevance to the optimal frequency of repeated treatments.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.418
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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.

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

Citations31
Published2018
Admission routes1
Has abstractyes

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