Shear Mode Bulk Acoustic Viscosity Sensor for Blood Coagulation Monitoring in Oral Anticoagulant Therapy
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".