Wireless mechanical and hybrid thrombus fragmentation of <i>ex vivo</i> endovascular thrombosis model in the iliac artery
Bibliographic record
Abstract
This study investigates the efficacy of an untethered magnetic robot (UMR) for wireless mechanical and hybrid blood clot removal in ex vivo tissue environments. By integrating x-ray-guided wireless manipulation with UMRs, we aim to address challenges associated with precise and controlled blood clot intervention. The untethered nature and size of these robots enhance maneuverability and accessibility within complex vascular networks, potentially improving clot removal efficiency. We explore mechanical fragmentation, chemical lysis, and hybrid dissolution techniques that combine mechanical fragmentation with chemical lysis, highlighting their potential for targeted and efficient blood clot removal. Through experimental validation using an ex vivo endovascular thrombosis model within the iliac artery of a sheep, we demonstrate direct revascularization of a 13-mm-long, 1-day-old blood clot positioned inside the left common iliac artery. This was achieved by deploying a UMR into the abdominal aorta within 15 min. Additionally, both mechanical fragmentation and hybrid dissolution achieve a greater volume rate of change compared to no intervention (control) and chemical lysis alone. Mechanical fragmentation exhibits clot removal with a median of 0.87 mm3/min and a range of 2.81 mm3/min, while the hybrid approach demonstrates slower but more consistent clot removal, with a median of 0.45 mm3/min and a range of 0.23 mm3/min.
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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) | 0.001 | 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".