Tracking and evaluation of dendritic cell migration by cellular magnetic resonance imaging
Bibliographic record
Abstract
Cellular magnetic resonance imaging (MRI) is a means by which cells labeled ex vivo with a contrast agent can be detected and tracked over time in vivo. This technology provides a noninvasive method with which to assess cell-based therapies in vivo. Dendritic cell (DC)-based vaccines are a promising cancer immunotherapy, but its success is highly dependent on the injected DC migrating to a secondary lymphoid organ such as a nearby lymph node. There the DC can interact with T cells to elicit a tumor-specific immune response. It is important to verify DC migration in vivo using a noninvasive imaging modality, such as cellular MRI, so that important information regarding the anatomical location and persistence of the injected DC in a targeted lymph node can be provided. An understanding of DC biology is critical in ascertaining how to label DC with sufficient contrast agent to render them detectable by MRI. While iron oxide nanoparticles provide the best sensitivity for detection of DC in vivo, a clinical grade iron oxide agent is not currently available. A clinical grade (19) Fluorine-based perfluorcarbon nanoemulsion is available but is less sensitive, and its utility to detect DC migration in humans remains to be demonstrated using clinical scanners presently available. The ability to quantitatively track DC migration in vivo can provide important information as to whether different DC maturation and activation protocols result in improved DC migration efficiency which will determine the vaccine's immunogenicity and ultimately the tumor immunotherapy's outcome in humans.
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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 | 0.001 | 0.000 |
| 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".