Active targeting of dendritic cells with mannan-decorated PLGA nanoparticles
Bibliographic record
Abstract
The purpose of this study was to identify an optimum targeted particulate formulation based on mannan (MN)-decorated poly(D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs), for efficient delivery of incorporated cargo to dendritic cells (DCs). In brief, NPs were formulated from two different types of PLGA; ester-terminated (capped) or COOH-terminated (uncapped) polymer. Incorporation of MN in NPs was achieved either through addition of MN during the process of NP formation or by attachment of MN onto the surface of the freeze dried NPs by physical adsorption or chemical conjugation (to COOH terminated polymer). The formulated NPs were characterized in terms of particle size, Zeta potential and level of MN incorporation. The effect of polymer type and the incorporation method on the extent of fluorescently labelled NP uptake by murine bone marrow-derived DCs have been investigated using flowcytometry. The results of this study showed MN incorporation to enhance the uptake of PLGA NPs by DCs. Among different MN incorporation strategies, covalent attachment of MN to COOH-terminated PLGA-NPs provided the highest level of MN surface decoration on NPs. Maximum NP uptake by DCs was achieved by COOH terminated PLGA NPs containing covalent or adsorbed MN. Therefore, a better chance of success for these formulations for active targeted drug and/or vaccine delivery to DCs is anticipated.
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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".