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Record W2492528640 · doi:10.1371/journal.pone.0159892

Focused Ultrasound-Induced Neurogenesis Requires an Increase in Blood-Brain Barrier Permeability

2016· article· en· W2492528640 on OpenAlexafffund
Skyler J. Mooney, Kairavi Shah, Sharon Yeung, Alison Burgess, Isabelle Aubert, Kullervo Hynynen

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsNeurogenesisMicrobubblesHippocampal formationUltrasoundBlood–brain barrierMedicineNeuroscienceBiomedical engineeringInternal medicineBiologyCentral nervous system

Abstract

fetched live from OpenAlex

Transcranial focused ultrasound technology used to transiently open the blood-brain barrier, is capable of stimulating hippocampal neurogenesis; however, it is not yet known what aspects of the treatment are necessary for enhanced neurogenesis to occur. The present study set out to determine whether the opening of blood-brain barrier, the specific pressure amplitudes of focused ultrasound, and/or the intravenous administration of microbubbles (phospholipid microspheres) are necessary for the enhancement of neurogenesis. Specifically, mice were exposed to burst (10ms, 1Hz burst repetition frequency) focused ultrasound at the frequency of 1.68MHz and with 0.39, 0.78, 1.56 and 3.0MPa pressure amplitudes. These treatments were also conducted with or without microbubbles, at 0.39 + 0.78MPa or 1.56 + 3.0MPa, respectively. Only focused ultrasound at the ~0.78 MPa pressure amplitude with microbubbles promoted hippocampal neurogenesis and was associated with an increase in blood-brain barrier permeability. These results suggest that focused ultrasound -mediated neurogenesis is dependent upon the opening of the blood-brain barrier.

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 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 score0.083
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.031
GPT teacher head0.215
Teacher spread0.185 · 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

Citations81
Published2016
Admission routes2
Has abstractyes

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