Magnetic micro/nano robots for physical cell stimulation: fundamentals, fabrication, and applications
Bibliographic record
Abstract
Abstract Cell stimulation plays a critical role in regulating essential cellular processes such as differentiation, migration, and apoptosis. Among various stimulation methods, magnetic micro/nano robots (mMNRs) have gained attention for their ability to precisely stimulate cellular and intracellular structures, owing to their structural flexibility and remote actuation. While much of the research on mMNRs has focused on their use for chemical stimulation of cells, particularly for targeted drug delivery, increasing attention is now given to their potential for physical stimulation of cells. As one form of physical stimulation, mechanical cell stimulation, particularly for cancer therapeutics, has been discussed in existing reviews. This article presents a comprehensive review of the most recent advances in mMNR-mediated physical cell stimulation, including mechanical, thermal, and electrical stimulation, and highlights their emerging roles in cancer therapy, regenerative medicine, neuromodulation, and antimicrobial treatment. The fundamentals of magnetic material-field interactions and actuation mechanisms are discussed, followed by fabrication strategies for structurally diverse mMNRs. Biomedical applications driven by mMNR-mediated cell stimulation are then discussed, along with remaining challenges and opportunities. By highlighting the unique capabilities of mMNRs in physical cell stimulation, this review emphasizes their potential in advancing both biological research and biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".