Developing PLGA nanoparticle platforms for intranasal vaccine delivery against respiratory pathogens at Alberta prairie clinical sites
Bibliographic record
Abstract
The challenge of vaccinating dispersed rural populations against respiratory pathogens influenza, respiratory syncytial virus, and emerging coronaviruses becomes sharply visible on the Alberta prairies, where cold-chain logistics and long travel distances to clinics undermine injectable vaccine coverage. This research developed poly (lactic-co-glycolic acid) (PLGA) nanoparticle platforms designed for intranasal delivery of a model protein antigen (ovalbumin, OVA) and assessed their immunogenic potential in a murine model at laboratory facilities affiliated with Alberta prairie clinical sites. PLGA nanoparticles were prepared by double emulsion-solvent evaporation at four PLGA copolymer ratios (50:50 and 75:25) and two molecular weights (24 kDa and 45 kDa), yielding eight formulations. The lead candidate (NP4: PLGA 75:25, 45 kDa) showed a mean particle diameter of 186.3 nm, a zeta potential of -21.8 mV, an antigen encapsulation efficiency of 64.7%, and sustained OVA release over 21 days. In BALB/c mice (n = 8 per group), intranasal administration of NP4 at days 0 and 14 elicited significantly higher nasal wash secretory IgA titers (log₁₀ 3.84±0.31) and serum IgG titers (log₁₀ 4.12±0.27) at day 28 compared with free OVA solution (IgA: log₁₀ 2.16±0.24; IgG: log₁₀ 2.74±0.33; p < 0.001 for both). These results demonstrate that PLGA nanoparticles can serve as an effective intranasal vaccine carrier platform, with potential applications for respiratory pathogen vaccines suited to the logistical constraints of Alberta's rural health infrastructure.
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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".