Magnetic Navigation Control of Microagents in the Vascular Network: Challenges and Strategies for Endovascular Magnetic Navigation Control of Microscale Drug Delivery Carriers
Bibliographic record
Abstract
Although navigation control has been applied in a multitude of environments, relatively little is known about the challenges and issues of navigation control in the vascular network. In an adult human, the vascular network consists of nearly 100,000 km of blood vessels, with diameters ranging from a few millimeters in the artery to just a few micrometers in the capillaries, and blood flow rates ranging from a few tens of centimeters per second to a few millimeters per second. Although vascular networks present great challenges, due to various environmental conditions, they are of special interest in medical microrobotics since they allow navigable agents to be delivered anywhere within the body. Controlled endovascular navigation would allow targeted surgical, diagnostic, and therapeutic interventions. In cancer therapy, for instance, although many of the most deadly cancers are initially located in a single region, modern therapies such as chemotherapy continue to inject excessive amounts of toxic agents thecirculate systematically throughout the vascular network. In general, only a tiny fraction of the drug reaches the treatment region [1]. Even the level of targeting achieved by agents with special coatings to enhance tumor cell specificity is far from optimal when they are injected systematically in the vascular network. Since the therapeutics do not discriminate between cancerous and healthy cells, systemic circulation of these agents must be avoided to eliminate, or at least minimize, secondary toxicity that affects healthy organs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".