Design and immunological evaluation of anti-CD205-tailored PLGA-based nanoparticulate cancer vaccine
Bibliographic record
Abstract
Sheikh Tasnim Jahan, Sams MA Sadat, Azita Haddadi Division of Pharmacy, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada Abstract: The aim of this research was to develop a targeted antigen–adjuvant assembled delivery system that will enable dendritic cells (DCs) to efficiently mature to recognize antigens released from tumor cells. It is important to target the DCs with greater efficiency to prime T cell immune responses. In brief, model antigen, ovalbumin (OV), and monophosphoryl lipid A adjuvant were encapsulated within the nanoparticle (NP) by double emulsification solvent evaporation method. Targeted NPs were obtained through ligand incorporation via physical adsorption or chemical conjugation process. Intracellular uptake of the NPs and the maturation of DCs were evaluated with flow cytometry. Remarkably, the developed delivery system had suitable physicochemical properties, such as particle size, surface charge, OV encapsulation efficiency, biphasic OV release pattern, and safety profile. The ligand modified formulations had higher targeting efficiency than the non-tailored NPs. This was also evident when the targeted formulations expressed comparatively higher fold increase in surface activation markers such as CD40, CD86, and major histocompatibility complex class II molecules. The maturation of DCs was further confirmed through secretion of extracellular cytokines compared to control cells in the DC microenvironment. Physicochemical characterization of NPs was performed based on the polymer end groups, their viscosities, and ligand-NP bonding type. In conclusion, the DC stimulatory response was integrated to develop a relationship between the NP structure and desired immune response. Therefore, the present study narrates a comparative evaluation of some selected parameters to choose a suitable formulation useful for in vivo cancer immunotherapy. Keywords: nanoparticle, immunotherapy, ovalbumin, monophosphoryl lipid A, dendritic cells
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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".