Swimming microorganisms acting as nanorobots versus artificial nanorobotic agents: A perspective view from an historical retrospective on the future of medical nanorobotics in the largest known three-dimensional biomicrofluidic networks
Bibliographic record
Abstract
The vascular system in each human can be described as a 3D biomicrofluidic network providing a pathway close to approximately 100 000 km in length. Such network can be exploited to target any parts inside the human body with further accessibility through physiological spaces such as the interstitial microenvironments. This fact has triggered research initiatives towards the development of new medical tools in the form of microscopic robotic agents designed for surgical, therapeutic, imaging, or diagnostic applications. To push the technology further towards medical applications, nanotechnology including nanomedicine has been integrated with principles of robotics. This new field of research is known as medical nanorobotics. It has been particularly creative in recent years to make what was and often still considered science-fiction to offer concrete implementations with the potential to enhance significantly many actual medical practices. In such a global effort, two main strategic trends have emerged where artificial and synthetic implementations presently compete with swimming microorganisms being harnessed to act as medical nanorobotic agents. Recognizing the potentials of each approach, efforts to combine both towards the implementation of hybrid nanorobotic agents where functionalities are implemented using both artificial/synthetic and microorganism-based entities have also been initiated. Here, through the main eras of progressive developments in this field, the evolutionary path being described from some of the main historical achievements to recent technological innovations is extrapolated in an attempt to provide a perspective view on the future of medical nanorobotics capable of targeting any parts of the human body accessible through the vascular 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".