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Record W2889806578 · doi:10.1166/jnn.2018.16425

Shear Mode Bulk Acoustic Viscosity Sensor for Blood Coagulation Monitoring in Oral Anticoagulant Therapy

2018· article· en· W2889806578 on OpenAlexaff
Shuren Song, Da Chen, Hongfei Wang, Qiuquan Guo, Wangli Yu

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2018
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceResonatorBlood viscosityCoagulationMiniaturizationBiomedical engineeringViscosityPiezoelectricityAcousticsComposite materialOptoelectronicsInternal medicineNanotechnologyMedicine

Abstract

fetched live from OpenAlex

Frequent monitoring of blood coagulation status is essential for patients receiving treatment with some oral anticoagulant agents. In this paper, we present a microelectromechanical film bulk acoustic resonator for the measurement of blood coagulation parameters. The resonator was made of an aluminum nitride piezoelectric film and operated in thickness shear resonance mode with a frequency of about 2 GHz. The resonant frequency showed a linear relationship with the viscosity of the environmental liquid over the wide range of 1-25 cP, falling within the physiological range of human blood. The coagulation process of human blood was monitored by following the frequency downshift due to the viscosity change. Therefore, the frequency response was used to determine quantitatively three clinically significant parameters being the enzymatic cascade time, the coagulation time and the clot degree. As a practical demonstration, the proposed micro-resonator was applied to monitor coagulation for one month in a patient taking the oral anticoagulant, warfarin, daily. The results measured by the resonator were consistent with those of the standard coagulometer. As a result of the excellent potential for integration, miniaturization and the availability of direct digital signals, the shear mode film bulk acoustic resonator has promising application for clinical and personal coagulation monitoring.

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 score0.089
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.282
Teacher spread0.259 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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