Magnetic Enrichment of Dendritic Cell Vaccine in Lymph Node with Fluorescent-Magnetic Nanoparticles Enhanced Cancer Immunotherapy
Bibliographic record
Abstract
Dendritic cell (DC) migration to the lymph node is a key component of DC-based immunotherapy. However, the DC homing rate to the lymphoid tissues is poor, thus hindering the DC-mediated activation of antigen-specific T cells. Here, we developed a system using fluorescent magnetic nanoparticles (-AP-fmNPs; loaded with antigen peptide, iron oxide nanoparticles, and indocyanine green) in combination with magnetic pull force (MPF) to successfully manipulate DC migration in vitro and in vivo. -AP-fmNPs endowed DCs with MPF-responsiveness, antigen presentation, and simultaneous optical and magnetic resonance imaging detectability. We showed for the first time that -AP-fmNP-loaded DCs were sensitive to MPF, and their migration efficiency could be dramatically improved both in vitro and in vivo through MPF treatment. Due to the enhanced migration of DCs, MPF treatment significantly augmented antitumor efficacy of the nanoparticle-loaded DCs. Therefore, we have developed a biocompatible approach with which to improve the homing efficiency of DCs and subsequent anti-tumor efficacy, and track their migration by multi-modality imaging, with great potential applications for DC-based cancer immunotherapy.
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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 | 0.000 | 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 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".