Dose Sparing of CpG Oligodeoxynucleotide Vaccine Adjuvants by Nanoparticle Delivery
Bibliographic record
Abstract
The main objective of these studies was to investigate whether the nanoparticle delivery has any immunopotentiation effect at modest doses of a few micro- or nanograms of CpG oligodeoxynucleotide (CpG ODN) and what would be the influence on T cell responses at such low doses. Various doses (5 to 0.05 microg) of a model CpG ODN adjuvant (#1826) along with 2 Lf tetanus toxoid (TT) were formulated in either nanoparticles using poly(D,L-lactic-co-glycolic acid) (PLGA) 50:50 co-polymer, or saline. Strong antigen specific ex vivo T cell proliferation was observed for the Balb/c mice receiving immunogens in nanoparticles. At 5 microg dose of CpG ODN, the T cell stimulation index (SI) was 241 as compared with 74 for the same dose when given in saline. Comparable SI value of 78 was observed at 100-fold lower dose (0.05 microg) using nanoparticles. Similarly, significantly higher (P<0.01) cytokine secretion was observed for nanoparticles groups. A ten-fold lower dose (0.5 microg instead of 5 microg) of CpG ODN in nanoparticles was adequate to obtain levels of IFN-gamma, TNF-alpha, and IL-2 comparable to those observed following immunisations in saline. The immunopotentiation effect of the particulate delivery on antibody response (total IgG and subtypes) was not so marked. These studies emphasise that antigen delivery in biodegradable nanoparticles can facilitate induction of strong T cell responses, particularly of the Th1 type, at extremely lower doses of CpG ODN. Such reduction in the effective dose would be advantageous for minimising the potential side effects of these novel adjuvants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".