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Record W3127034168 · doi:10.1016/j.pdpdt.2020.102173

The synergistic effect of focused ultrasound and biophotonics to overcome the barrier of light transmittance in biological tissue

2021· article· en· W3127034168 on OpenAlexaff
Jae Hyuk Kim, Jaewoo Shin, Chanho Kong, Sungho Lee, Won Seok Chang, Seung Hee Han

Bibliographic record

VenuePhotodiagnosis and Photodynamic Therapy · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiophotonicsTransmittanceMaterials scienceDeformation (meteorology)Light scatteringTransmission (telecommunications)Monte Carlo methodScatteringBiological tissueUltrasoundOpticsPenetration (warfare)AcousticsBiomedical engineeringOptoelectronicsComposite materialPhotonicsPhysicsComputer scienceTelecommunicationsMedicineEngineering

Abstract

fetched live from OpenAlex

Optical technology is a tool to diagnose and treat human diseases. Shallow penetration depth caused by the high optical scattering nature of biological tissues is a significant obstacle to utilizing light in the biomedical field. In this paper, light transmission enhancement in the rat brain induced by focused ultrasound (FUS) was observed and the cause of observed enhancement was analyzed. Both air bubbles and mechanical deformation generated by FUS were cited as the cause. The Monte Carlo simulation was performed to investigate effects on transmission by air bubbles and finite element method was also used to describe mechanical deformation induced by motions of acoustic particles. As a result, it was found that the mechanical deformation was more suitable to describe the transmission change according to the FUS pulse observed in the experiment.

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 score0.011
Threshold uncertainty score0.584

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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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