Kidney Stone Dissolution By Tetherless, Enzyme‐Loaded, Soft Magnetic Miniature Robots
Bibliographic record
Abstract
Kidney stones are some of the most common urinary diseases, affecting 12% of the population. The high prevalence and recurrence of this disease urges the development of more targeted and effective treatment with lower side effects and less invasiveness avoiding recurrence and prolonged drug administration. Particularly for recurring stone formers, the current methods of persistent drug treatment and repetitive surgeries for stone removal are unsatisfying solutions that bring a huge burden to the patients and healthcare systems. For these reasons, a delivery strategy that provides drug administration at the disease site through active, wireless transport is urgently needed to improve urinary tract disease treatment. A wireless treatment of kidney stones is proposed with the help of flexible, magnetically steered, enzymatically active robots. These robots are designed to navigate in the urinary tract and locally dissolve the stones by action of embedded urease. The robots are made of millimeter-sized, gelatin-based polymer strips with embedded micromagnets and encapsulated urease which constantly converts urea to increase urinary pH. This study demonstrates enhanced stone dissolution and the robots' magnetic navigation through the different parts of a 3D printed human urinary tract model. A clinical ultrasound system allows real-time localization of the robots. This research proposes a less invasive and more targeted strategy for medical interventions in the urinary tract with potential to circumvent surgery in case of uric acid kidney stones, which is relevant especially in the light of high prevalence and recurrence of kidney stones.
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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".