Low frequency mechanical actuation accelerates reperfusion in-vitro
Bibliographic record
Abstract
BACKGROUND: Rapid restoration of vessel patency after acute myocardial infarction is key to reducing myocardial muscle death and increases survival rates. Standard therapies include thrombolysis and direct PTCA. Alternative or adjunctive emergency therapies that could be initiated by minimally trained personnel in the field are of potential clinical benefit. This paper evaluates a method of accelerating reperfusion through application of low frequency mechanical stimulus to the blood carrying vessels. MATERIALS AND METHOD: We consider a stenosed, heparinized flow system with aortic-like pressure variations subject to direct vessel vibration at the occlusion site or vessel deformation proximal and distal to the occlusion site, versus a reference system lacking any form of mechanical stimulus on the vessels. RESULTS: The experimental results show limited effectiveness of the direct mechanical vibration method and a drastic increase in the patency rate when vessel deformation is induced. For vessel deformation at occlusion site 95% of clots perfused within 11 minutes of application of mechanical stimulus, for vessel deformation 60 centimeters from the occlusion site 95% percent of clots perfused within 16 minutes of stimulus application, while only 2.3% of clots perfused within 20 minutes in the reference system. CONCLUSION: The presented in-vitro results suggest that low frequency mechanical actuation applied during the pre-hospitalization phase in patients with acute myocardial infarction have potential of being a simple and efficient adjunct therapy.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".