PLGA nanoparticles for oral delivery of prion-specific antigen: a novel approach to chronic wasting disease vaccination
Bibliographic record
Abstract
Prion diseases, such as chronic wasting disease (CWD), are incurable, fatal neurodegenerative disorders. We have developed a recombinant dimeric deer prion protein (Ddi) vaccine against CWD that has shown promising immune responses when injected subcutaneously (s.c). While s.c injection is suitable for controlled conditions, oral administration is practical in wildlife. Herein, we have developed an oral vaccine utilizing poly lactic co-glycolic acid (PLGA) nanoparticles, co-encapsulating Ddi and oligodeoxynucleotide adjuvant (CpG) using double emulsion-solvent evaporation technique. Our results showed production of spherical PLGA nanoparticles with size of ~ 200–300 nm, an acceptable surface charge (− 14.2 ± 5.73 mV), and an encapsulation efficiency of approximately 70 and 30%, for Ddi and CpG, respectively. We administered the developed vaccine to FVB mice orally and subcutaneously, followed by ELISA assays of the sera and feces. Mice receiving the vaccine subcutaneously exhibited high antibody reactivities to the used antigen in their sera (100% positivity), with no detectable positive reactivity in their feces. However, those receiving the oral vaccine showed 60 and 80% positivity in sera and feces, respectively, indicating specific mucosal immunity. We also found specific T cell reactivity in mice immunized orally. This approach is paving the way for developing an oral vaccine against CWD.
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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".