Semiquantitation of Mouse Dendritic Cell Migration In Vivo Using Cellular MRI
Bibliographic record
Abstract
Despite recent therapeutic advances, including the introduction of novel cytostatic drugs and therapeutic antibodies, many cancer patients will experience recurrent or metastatic disease. Current treatment options, particularly for those patients with metastatic breast, prostate, or skin cancers, are complex and have limited curative potential. Recent clinical trials, however, have shown that cell-based therapeutic vaccines may be used to generate broad-based, antitumor immune responses. Dendritic cells (DC) have proved to be the most efficacious cellular component for therapeutic vaccines, serving as both the adjuvant and antigen delivery vehicle. At present it is not possible to noninvasively determine the fate of DC-based vaccines after their administration to human subjects. In this study, we demonstrate that in vitro-generated mouse DC can be readily labeled with superparamagnetic iron oxide nanoparticles, Feridex, without altering cell morphology, or their phenotypic and functional maturation. Feridex-labeling enables the detection of DC in vivo after their migration to draining lymph nodes using a 1.5 T clinical magnetic resonance scanner. In addition, we report a semiquantitative approach for analysis of magnetic resonance images and show that the Feridex-induced signal void volume, and fractional signal loss, correlates with the delivery and migration of small numbers of in vitro-generated DC. These findings, together with ongoing preclinical studies, are key to gaining information critical for improving the efficacy of therapeutic vaccines for the treatment cancer, and potentially, chronic infectious diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 teacher head, 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".