Tumor targeting by computer controlled guidance of Magnetotactic Bacteria acting like autonomous microrobots
Bibliographic record
Abstract
This paper reports the successful navigation of Magnetotactic Bacteria (MTB) towards regions located inside a solid tumor using a computer controlled set of magnetic coils. MTB uses two flagella bundles connected to rotary molecular motors as a propulsion system enabling them to reach swimming velocities of 300µm·s−1without external source of power. Acting like autonomous microrobots, they can be remotely controlled by an appropriate magnetic guidance system as their swimming direction is predominantly determined by the direction of the ambient magnetic field. In order to cope with the harsh environment of the solid tumor and to bypass the lack of knowledge of the internal vessels architecture forming the route to the tumor, fundamental MTB motion properties are taken into account in addition to their ability to swim along the magnetic field. The studies revealed the presence of these bacteria in the necrotic zone of a solid tumor. Preliminary results suggest that not only the magnetic guidance can help enhancing the uniform distribution of MTB inside the tumor for therapeutic or diagnostic purposes, but the experimental data showed that they could perform accurately and efficiently under computer control, many of the tasks previously envisioned for future synthetic microrobots of only 1 to 2 micrometers in diameter and designed to operate in the human microvascular network.
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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) | 0.000 | 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".