Magnetically Actuated Capsule Mechanism for Drug Delivery, Sampling, and Cargo Transport in the Gastrointestinal Tract
Bibliographic record
Abstract
Wireless capsule endoscopes are often limited to imaging applications and can lack active control capabilities. Over the last two decades, a growing body of research in medical robotics has introduced active actuation. Embedding magnetic components inside these devices is one of the ways to achieve this; however, most of these tools are still limited to a single function, such as drug delivery, sampling, or imaging. Multifunctional capsules that can perform several different tasks can be used for a range of biomedical applications, leading to easy adoption due to their versatility. In this study, we present a novel magnetically actuated capsule with a spring-magnet mechanism designed for drug delivery, microbiome sampling, and cargo transport. The capsule is remotely actuated using external magnetic fields generated by a permanent magnet. It can be ingested orally, activated at a target location for drug delivery, microbiome sampling, or transporting cargo, and expelled naturally. A mathematical model is developed to optimize the mechanism's design. We demonstrate the capsule's multi-functional capabilities through successful drug delivery, sampling, and cargo transport experiments in a 3D printed maze. We also demonstrate capsule navigation in a stomach phantom. This unique mechanism can be adapted and integrated into a range of microrobotic devices, expanding their functionality and clinical utility.
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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".