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Record W2389797858 · doi:10.1121/1.4946045

The microscale evolution of the erosion front of blood clots exposed to ultrasound stimulated microbubbles

2016· article· en· W2389797858 on OpenAlexafffund
Christopher Acconcia, Ben Y. C. Leung, David E. Goertz

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroscale chemistryFibrinMaterials scienceMicrobubblesCoulter counterBiomedical engineeringErosionFront (military)UltrasoundGeologyMedicineRadiologyBiology

Abstract

fetched live from OpenAlex

Serial two-photon microscopy of blood clots with fluorescently tagged fibrin networks was conducted during microbubble-mediated sonothrombolysis to examine the microscale evolution of the resulting erosion front. The development of a complex zonal erosion pattern was observed, comprised of a cell depleted layer of fibrin network overlying intact clot which then underwent progressive recession. The fibrin zone architecture was dependent on exposure conditions with 0.1 MPa causing no erosion, 0.39 MPa resulting in homogenous structure, and combination 0.39/0.96 MPa pulses forming large-scale tunnels. High speed imaging and Coulter counter data indicated the fibrin zone formation process involves the ejection of intact erythrocytes.

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 score0.003

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 integrity0.0000.001
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

Explore more

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