Immunoadjuvant activity of the nanoparticles’ surface modified with mannan
Bibliographic record
Abstract
Mannan (MN) is the natural ligand for mannose receptors, which are widely expressed on dendritic cells (DCs). The purpose of this study was to assess the effect of formulation parameters on the immunogenicity of MN-decorated poly (D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs) in terms of their ability to stimulate DC phenotypic as well as functional maturation. For this purpose, NPs were formulated from either ester-terminated or COOH-terminated PLGA. Incorporation of MN in NPs was achieved through encapsulation, physical adsorption or chemical conjugation. Murine bone marrow derived DCs (BMDCs) were treated with various NP formulations and assessed for their ability to up-regulate DC cell surface markers, secrete immunostimulatory cytokines and to activate allogenic T cell responses. DCs treated with COOH-terminated PLGA-NPs containing chemically conjugated MN (MN-Cov-COOH) have shown superior performance in improving DC biological functions, compared to the rest of the formulations tested. This may be attributed to the higher level of MN incorporation in the former formulation. Incorporation of MN in PLGA NPs through chemical conjugation can lead to enhanced DC maturation and stimulatory function. This strategy may be used to develop more effective PLGA-based vaccine formulations.
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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".