Labelling dendritic cells with SPIO has implications for their subsequent <i>in vivo</i> migration as assessed with cellular MRI
Bibliographic record
Abstract
An optimized non-invasive imaging modality capable of tracking and quantifying in vivo DC migration in patients would provide clinicians with valuable information regarding therapeutic DC-based vaccine outcomes. Superparamagnetic iron oxide (SPIO) nanoparticles were used to label bone marrow-derived DC. In vivo DC migration was tracked and quantified non-invasively using cellular magnetic resonance imaging (MRI) in a mouse model. Labelling DC with SPIO reflects the kinetics of DC migration in vivo but appears to reduce overall DC migration, in part due to nanoparticle size. Magnetic separation of SPIO-labelled (SPIO(+)) DC from unlabelled (SPIO(-)) DC prior to injection improves SPIO(+) DC migration to the lymph node. Corresponding MR image data better correlate with the presence of DC in vivo; an improved immunological response is also seen. Cellular MRI is a viable, non-invasive imaging tool that can routinely track DC migration in vivo. Consideration should be given to optimizing MRI contrast agent-labelling of clinical-grade DC in order to accurately correlate DC fate to immunological outcomes in patients.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".