Liposome/DNA complexes coated with biodegradable PLA improve immune responses to plasmid encoding hepatitis B surface antigen
Bibliographic record
Abstract
We hypothesized that the addition of polymer to the surface of liposome/DNA complexes may potentially enhance in vivo delivery of plasmid DNA to antigen-presenting cells and thereby facilitate enhanced immune responses to encoded protein. BALB/c mice were immunized subcutaneously or intramuscularly three times with a total of 50 microg of the plasmid pRc/CMV-HBs(S) (ayw subtype) encoding for the hepatitis B surface antigen. We measured transgene-specific total immunoglobulin G (IgG), IgG2a, IgG2b and IgG1 antibody responses as well as splenocyte and T-cell proliferation and cytokine production upon re-stimulation following immunization. Modification of lipid/DNA complexes by the polymer precipitation method used here for the addition of poly(d,l-lactic acid) was found to be consistently and significantly more effective than either unmodified liposomal DNA or naked DNA in eliciting transgene-specific immune responses to plasmid-encoded antigen when administered by the subcutaneous route. In addition, the polymer-modified formulations delivered by this route were more effective than naked DNA delivered by the intramuscular route in inducing antibody responses (n=5, P<0.03). Our observations provide 'proof of principle' for the use of these multicomponent formulations, which offer potential for manipulation and increased transfection efficiency in vivo for the purposes of genetic immunization.
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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".