Towards MR-navigable nanorobotic carriers for drug delivery into the brain
Bibliographic record
Abstract
Magnetic Resonance Navigation (MRN) relies on Magnetic Nanoparticles (MNPs) embedded in microcarriers or microrobots to allow the induction of a directional propelling force by 3-D magnetic gradients. These magnetic gradients are superposed on a sufficiently high homogeneous magnetic field (e.g. the Bo field of a MR scanner) to achieve maximum propelling force through magnetization saturation of the MNPs. As previously demonstrated by our group, such technique was successful at maintaining microcarriers along a planned trajectory in the blood vessels based on tracking information gathered using Magnetic Resonance Imaging (MRI) sequences from artifacts caused by the same MNPs. Besides propulsion and tracking, the same MNPs can be synthesized with characteristics that can allow for the diffusion of therapeutic cargo carried by these MR-navigable carriers through the Blood Brain Barrier (BBB) using localized hyperthermia without compromising the MRN capabilities. In the present study, localized hyperthermia induced by an alternating magnetic field (AC field) is investigated for the purpose of transient controlled disruption of the BBB and hence local delivery of therapeutic agents into the brain. Here, an external heating apparatus was used to impose a regional heat shock on the skull of a living mouse model. The effect of heat on the permeability of the BBB was assessed using histological observation and tissue staining by Evans blue dye. Results show direct correlation between hyperthermia and BBB leakage as well as its recovery from thermal damage. Therefore, in addition to on-command propulsion and remote tracking, the proposed navigable agents could be suitable for controlled opening of the BBB by hyperthermia and selective brain drug delivery.
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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".