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Record W4240118754 · doi:10.32920/ryerson.14648499

Effect of pulsed high intensity focused ultrasound on the nerve compound action potential amplitude and conduction velocity

2021· preprint· en· W4240118754 on OpenAlexaff
Steve Tran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsUltrasoundNerve conduction velocityIntensity (physics)High-intensity focused ultrasoundVentral nerve cordPeripheral nerveCompound muscle action potentialAnatomyBiomedical engineeringElectrophysiologyMedicinePhysicsInternal medicineOpticsRadiologyCentral nervous system

Abstract

fetched live from OpenAlex

Therapeutic HIFU has been used as a non-invasive energy modality to compromise nerve function since the 1950s. Several contributions have been made in recent years to characterize these effects on nerve function. In this study, short repeated bursts of HIFU, termed as pulsed high intensity focused ultrasound (pHIFU), was directed at nerve tissue. The pHIFU transducer operated at a central frequency of 1.95 MHz and had a focal length of approximately 12 cm. The ventral nerve cord from the American Lobster (Homarus americanus), n=15, was sonicated cumulatively at 3 exposure times: 1s, 6s, and 16s, at an intensity of 1010 W/cm2, or focal pressure of 5.51 MPa. The compound action potential (CAP) and conduction velocity (CV) were seen to decrease as sonication exposure time to the nerve increased. The experiments performed demonstrate the feasibility to modulate nerve CAP and nerve CV using non-thermal mechanisms of ultrasound.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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