Evaluation of Solid Lipid Nanoparticles as Carriers for Delivery of Hepatitis B Surface Antigen for Vaccination Using Subcutaneous Route
Bibliographic record
Abstract
PURPOSE: Solid lipid nanoparticles (SLN) have emerged as carriers for therapeutic peptides, proteins, antigens and bioactive molecules. We have explored the potential of SLN as carrier for Hepatitis B surface antigen (HBsAg) by surface modifications to enhance their loading efficiency and the cellular uptake, using subcutaneous route. METHODS: Four different formulations of SLN were prepared by solvent injection method and characterized for various physical properties: particle size, surface morphology, shape, zeta potential, polydispersity, X-ray diffraction analysis, release profile and entrapment efficiency. HBsAg loaded SLN were studied for their functional characteristics, in vitro cellular uptake and internalization studies by human dendritic cells, macrophages and fibroblasts, T cell proliferation and TH1/TH2 response. Humoral immune response elicited by subcutaneously administered HBsAg containing SLN formulations were studied in vivo in mice. RESULTS: Compared to soluble HBsAg; SLN, particularly the mannosylated formulation, showed better cellular uptake, lesser cytotoxicity and induction of greater TH1 type of immune response. They also showed better immunological potential by producing sustained antibody titer. CONCLUSION: Mannosylated SLN appears to be promising as carrier for vaccine delivery against hepatitis B as ascertained by in vitro and in vivo studies, however further investigations on humans are required to establish their potential as vaccines against hepatitis B infection.
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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".