Thrombus Boring Microrobot Prepared by an Integrated Phase Separation and Interfacial Self‐Assembly Process Toward Thrombolysis
Bibliographic record
Abstract
The pathological formation of thrombi is the primary etiological factor of acute cardiovascular and cerebrovascular diseases, accounting for one-quarter of global fatalities. Traditional thrombolytic drugs are constrained by short half-life, low utilization, and severe complications. Inspired by the tunnel boring machine to excavate strata into small rocks, we report urokinase plasminogen activator (uPA)-modified thrombus boring microrobots (uTBMs), prepared by a one-step integrated phase separation and interfacial self-assembly process, for effective thrombolysis. The uTBMs are composed of microspheres capped with dual-layered structures of magnetic nanoparticles (MNPs) and cilia nanostructures. In situ observation reveals the integrated phase separation and interfacial self-assembly process of the uTBM within an emulsion droplet. The capped layer of MNPs allows for controllable motion and rotation behavior under the manipulation of a remote magnetic field. The uPA-modified cilia nanostructures grasp and degrade the fibrin network, synergizing with the uTBMs rotation to mechanically excavate blood cells from thrombus individually, achieving ∼8.5-fold higher thrombolytic efficacy than uPA alone. This research demonstrates the feasibility of controllably fabricating and modifying complex-structured microrobots via the simple process toward potential thrombus 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) | 0.000 | 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".