Visualizing tumour self-homing with magnetic particle imaging
Bibliographic record
Abstract
Due to their innate tumour homing capabilities, in recent years, circulating tumour cells (CTCs) have been engineered to express therapeutic genes for targeted treatment of primary and metastatic lesions. Additionally, previous studies have incorporated optical or PET imaging reporter genes to enable noninvasive monitoring of therapeutic CTCs in preclinical tumour models. An alternative method for tracking cells is to pre-label them with imaging probes prior to transplantation into the body. This is typically more sensitive to low numbers of cells since large amounts of probe can be concentrated in each cell. The objective of this work was to evaluate magnetic particle imaging (MPI) for the detection of iron-labeled experimental CTCs. CTCs were labeled with micro-sized iron oxide (MPIO) particles, administered via intra-cardiac injection in tumour bearing mice and were detected in the tumour region of the mammary fat pad. Iron content and tumour volumes were calculated. Ex vivo MPI of the tumours and immunohistochemistry were used to validate the imaging data. Here, we demonstrate for the first time the ability of MPI to sensitively detect systemically administered iron-labeled CTCs and to visualize tumour self-homing in a murine model of human breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".