Dual Ultrasound and Photoacoustic Tracking of Magnetically Driven Micromotors: From In Vitro to In Vivo
Bibliographic record
Abstract
The fast evolution of medical micro- and nanorobots in the endeavor to perform non-invasive medical operations in living organisms has boosted the use of diverse medical imaging techniques in the last years. Among those techniques, photoacoustic imaging (PAI), considered a functional technique, has shown to be promising for the visualization of micromotors in deep tissue with high spatiotemporal resolution as it possesses the molecular specificity of optical methods and the penetration depth of ultrasound. However, the precise maneuvering and function's control of medical micromotors, in particular in living organisms, require both anatomical and functional imaging feedback. Therefore, herein, the use of high-frequency ultrasound and PAI is reported to obtain anatomical and molecular information, respectively, of magnetically-driven micromotors in vitro and under ex vivo tissues. Furthermore, the steerability of the micromotors is demonstrated by the action of an external magnetic field into the uterus and bladder of living mice in real-time, being able to discriminate the micromotors' signal from one of the endogenous chromophores by multispectral analysis. Finally, the successful loading and release of a model cargo by the micromotors toward non-invasive in vivo medical interventions is demonstrated.
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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 | 0.001 | 0.001 |
| 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".