Magnetic Resonance Propulsion, Control and Tracking at 24 Hz of an Untethered Device in the Carotid Artery of a Living Animal: An Important Step in the Development of Medical Micro- and Nanorobots
Bibliographic record
Abstract
Our recent demonstration of a ferromagnetic bead being navigated automatically inside the carotid artery of a living animal at an average speed of 10 cm/s using a clinical MRI system may be considered as a significant step in the field of medical micro- and nanorobotics. This is particularly true when we consider that an appropriate tracking method was embedded in the closed-loop control process allowing the blood vessels to be considered as navigational routes, providing maximum access for conducting operations inside the human body. But more importantly, this demonstration not only validates preliminary theoretical models but provides us with initial insights about the strategies and approaches that are likely to be used to navigate under computer control, micro- and nanodevices including nanorobots from the largest to the smallest diameter blood vessels that could be used to reach targets inside the human body. Here, based on these initial experimental data obtained in vivo, such strategies and methods are briefly described with some initial design concepts of medical interventional micro- and nanorobots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".