“Pathogen-Mimicking” Nanoparticles for Vaccine Delivery to Dendritic Cells
Bibliographic record
Abstract
A clinically relevant delivery system that can efficiently target and deliver antigens and adjuvant to dendritic cells (DCs) is under active investigation. Immunization with antigens and immunomodulators encapsulated in poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles elicits potent cellular immune responses; but understanding how this mode of delivery affects DCs and priming of naive T cells needs further investigation. In the current study, we assessed the extent of maturation of DCs after treatment with monophosphoryl lipid A (MPLA) encapsulated in PLGA nanoparticles and the generation of primary T-cell immune responses elicited by DCs loaded with antigens using this approach. Results indicated that DCs up-regulated the expression of surface maturation markers and demonstrated an enhanced allostimulatory capacity after treatment with MPLA containing PLGA nanoparticles. Treatment of DCs with MPLA containing nanoparticles released high amounts of proinflammatory and TH1 (T helper 1) polarizing cytokines and chemokines greater than that achieved by MPLA in solution. The delivery of ovalbumin in PLGA nanoparticles to DCs induced potent in vitro and in vivo antigen-specific primary TH1 immune responses that were furthermore enhanced with codelivery of MPLA along with the antigen in the nanoparticle formulation. Delivery of MUC1 lipopeptide (BLP25, a cancer vaccine candidate) and MPLA in PLGA nanoparticles to human DCs induced proliferation of MUC1 reactive T cells in vitro demonstrating the break in tolerance to self-antigen MUC1. These results demonstrated that targeting antigens along with toll-like receptor ligands in PLGA nanoparticles to DCs is a promising approach for generating potent TH1 polarizing immune responses that can potentially override self-tolerance mechanisms and become beneficial in the immunotherapy of cancer and infectious diseases.
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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".