Tomographic magnetic particle imaging of cancer targeted nanoparticles
Bibliographic record
Abstract
Magnetic Particle Imaging (MPI) is an emerging, whole body biomedical imaging technique, with sub-millimeter spatial resolution and high sensitivity to a biocompatible contrast agent consisting of an iron oxide nanoparticle core and a biofunctionalized shell. Successful application of MPI for imaging of cancer depends on the nanoparticles (NPs) accumulating at tumors at sufficient levels relative to other sites. NPs' physiochemical properties such as size, crystallographic structure and uniformity, surface coating, stability, blood circulation time and magnetization determine the efficacy of their tumor accumulation and MPI signal generation. Here, we address these criteria by presenting strategies for the synthesis and surface functionalization of efficient MPI tracers, that can target a typical murine brain cancer model and generate three dimensional images of these tumors with very high signal-to-noise ratios (SNR). Our results showed high contrast agent sensitivities that enabled us to detect 1.1 ng of iron (SNR ∼ 3.9) and enhance the spatial resolution to about 600 μm. The biodistribution of these NPs was also studied using near-infrared fluorescence (NIRF) and single-photon emission computed tomography (SPECT) imaging. NPs were mainly accumulated in the liver and spleen and did not show any renal clearance. This first pre-clinical study of cancer targeted NPs imaged using a tomographic MPI system in an animal model paves the way to explore new nanomedicine strategies for cancer diagnosis and therapy, using clinically safe magnetic iron oxide nanoparticles and MPI.
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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) | 0.001 | 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".