“Pathogen-Mimicking” Nanoparticles for Vaccine Delivery to Dendritic Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it