Cellular MRI as a suitable, sensitive non-invasive modality for correlating in vivo migratory efficiencies of different dendritic cell populations with subsequent immunological outcomes
Bibliographic record
Abstract
The clinical application of dendritic cells (DC) as adjuvants in immunotherapies such as the cell-based cancer vaccine continues to gain interest. The overall efficacy of this emerging immunotherapy, however, remains low. Studies suggest the stage of maturation and activation of ex vivo-prepared DC immediately prior to patient administration is critical to subsequent DC migration in vivo, which ultimately affects overall vaccine efficacy. While it is possible to generate mature and activated DC ex vivo using various stimulatory cocktails, in the case of cancer patients, the qualitative and quantitative assessment of which DC stimulatory cocktail works most effectively to enhance subsequent DC migration in vivo is difficult. Thus, a non-invasive imaging modality capable of monitoring the real-time migration of DC in long-term studies is required. In this paper, we address whether cellular magnetic resonance imaging (MRI) is sufficiently sensitive to quantitatively detect differences in the migratory abilities of two different DC preparations: untreated (resting) versus ex vivo matured in a mouse model. In order to distinguish our ex vivo-generated DC of interest from surrounding tissues in magnetic resonance (MR) images, DC were labeled in vitro with the superparamagnetic iron oxide (SPIO) nanoparticle FeREX®. Characterization of DC phenotype and function following addition of a cytokine maturation cocktail and the toll-like receptor ligand CpG, both in the presence and in the absence of SPIO, were also carried out. Conventional histological techniques were used to verify the quantitative data obtained from MR images. This study provides important information relevant to tracking the in vivo migration of ex vivo-prepared and stimulated DC.
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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 | 0.001 | 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 | 0.001 |
| 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".