Co-Encapsulated CpG Oligodeoxynucleotides and Ovalbumin in PLGA Microparticles; An in vitro and in vivo Study
Bibliographic record
Abstract
PURPOSE: The objective of this work was to evaluate the effect in the immune response produced by CpG oligodeoxynucleotides (ODN) co-encapsulated with the antigen ovalbumin (OVA) within poly(lactic-co-glycolic) acid (PLGA) 502 and 752 microparticles (MP). METHODS: MP were prepared by blending 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) with PLGA and Total Recirculation One Machine System (TROMS) technology and contained OVA along with CpG sequences associated to DOTAP. After confirming the integrity of both encapsulated molecules, BALB/c mice were immunized with the resulting MP and OVA-specific antibodies and cytokine production were assessed in order to determine the immunological profile induced in mice. RESULTS: One m near non-charged MP co-encapsulated very efficiently both OVA and CpG ODN. The release of both OVA and CpG was slow and incomplete irrespective of polymer. The results of the immune response induced in BALB/c mice indicated that, depending on the PLGA polymer used, co-encapsulation did not improve the immunogenicity of the antigen, compared either with the simply co-administration of both antigen and CpG, or with the microencapsulated antigen. Thus, mice immunized with OVA associated to PLGA 756 displayed an IgG2a characterized response which was biased to an IgG1 profile in case of CpG co-encapsulation. On the contrary, the co-encapsulation of CpG with OVA into PLGA 502 significantly improved the isotype shifting in comparison with the one showed by mice immunized with OVA loaded PLGA 502. CONCLUSION: This study underlines the importance of MP characteristics to fully exploit simultaneous antigen and CpG ODN particulate delivery as effective vaccine construct.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.001 | 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".