Enhanced antigen‐specific primary CD4<sup>+</sup> and CD8<sup>+</sup> responses by codelivery of ovalbumin and toll‐like receptor ligand monophosphoryl lipid A in poly(<scp>D,L</scp>‐lactic‐<i>co</i>‐glycolic acid) nanoparticles
Bibliographic record
Abstract
The purpose of this research was to investigate the use of biodegradable poly(D,L-lactic-co-glycolic acid) nanoparticles (PLGA-NP) as a vaccine delivery system to codeliver antigen, ovalbumin (OVA) along with monophosphoryl lipid A (MPLA) as adjuvant for induction of potent CD4(+) and CD8(+) T cell responses. The primary CD4(+) T responses to OVA/MPLA NP were investigated using OVA-specific T cells from DO11.10 transgenic mice. Following adoptive transfer of these cells, mice were immunized s.c. by NP formulations. For assessing the CD8(+) responses, bone marrow derived dendritic cells (DCs) were pulsed with different OVA formulations, then, cocultured with CD8(+) T cells from OT-1 mice. T cell proliferation/activation and IFN-gamma secretion profile have been examined. Particulate delivery of OVA and MPLA to the DCs lead to markedly increase in in vitro CD8(+) T cell T cell proliferative responses (stimulation index >3000) and >13-folds increase in in vivo clonal expanded CD4(+) T cells. The expanded T cells were capable of cytokine secretion and expressed an activation and memory surface phenotype (CD62L(lo), CD11a(hi), and CD44(hi)). Codelivery of antigen and MPLA in PLGA-NP offers an effective method for induction of potent antigen specific CD4(+) and CD8(+) T cell responses.
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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".