Flow-driven magnetic microcatheter for superselective arterial embolization
Bibliographic record
Abstract
Minimally invasive interventions performed inside brain vessels with the synergistic use of microcatheters pushed over guidewires have revolutionized the way aneurysms, strokes, arteriovenous malformations, brain tumors, and other cerebrovascular conditions are being treated. However, a substantial portion of the brain vasculature remains inaccessible because the conventional catheterization technique based on transmitting forces from the proximal to the distal end of the instruments imposes stringent constraints on their diameter and stiffness. Here, we overcame this mechanical barrier by microengineering ultraminiaturized magnetic microcatheters in the form of an inflatable flat tube, making them ultraflexible and capable of harnessing the kinetic energy of blood flow for endovascular navigation. We introduce a compact and versatile magnetic steering platform that is compatible with conventional biplane fluoroscope imaging and demonstrate safe and effortless navigation and tracking of hard-to-reach, distal, tortuous arteries that are as small as 180 micrometers in diameter with a curvature radius as small as 0.69 millimeters. Furthermore, we demonstrate the superselective infusion of contrast and embolic liquid agents, all in a porcine model. These results pave the way to reach, diagnose, and treat currently inaccessible distal arteries that may be at risk of bleeding or feeding a tumor. Our endovascular technology can also be used to selectively target tissues for drug or gene delivery from within the arteries, not only in the central and peripheral nervous systems but also in almost any other organ system, with improved accuracy, speed, and safety.
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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".