Directed Navigation of Magnetotactic Bacteria via Magnetotaxis in a 3D Vasculature‐On‐A‐Chip
Bibliographic record
Abstract
Abstract Magnetotactic bacteria (MTB), inherently motile and self‐powered, are promising biorobotic candidates for targeted anti‐cancer drug delivery since they can actively deliver the therapeutic agent to the tumor, decreasing adverse side effects. However, the directed navigation of these bacteria through intricate microenvironments mimicking the natural microvasculature has not been investigated. Here, the directed navigation of MTB is demonstrated within a vasculature‐on‐a‐chip platform. A perfusable vascular network is developed to investigate MTB at the single‐microorganism level. MTB is demonstrated to successfully align and navigate along the magnetic field inside the microvessels. Surface interaction with the microvessel walls, hydrodynamic forces, and counterdirectional flows in the order of 10 µm∙s −1 are examined as potential factors that may interfere with the MTB alignment and magnetotaxis. The average swimming speed of the studied bacteria within the vasculature‐on‐a‐chip device is 13.9 µm s −1 . Finite Element Analysis reveals that under these conditions, MTB experience shear stresses of up to 30 Pa, and drag forces between 10 and 40 pN, depending on their relative orientation to the flow field. Altogether, this work provides a first demonstration of effective directed navigation of MTB in a vasculature‐on‐a‐chip platform, and the influence of external factors on their field alignment and magnetotactic behavior.
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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".