Cationic solid–lipid nanoparticles are as efficient as electroporation in <scp>DNA</scp> vaccination against visceral leishmaniasis in mice
Bibliographic record
Abstract
The use of an appropriate delivery system has recently emerged as a promising approach for the development of effective vaccination against visceral leishmaniasis (VL). Here, we compare two vaccine delivery systems, namely electroporation and cationic solid-lipid nanoparticle (cSLN) formulation, to administer a DNA vaccine harbouring the L. donovani A2 antigen along with L. infantum cysteine proteinases [CPA and CPB without its unusual C-terminal extension (CPB(-CTE) )] and evaluate their potential against L. infantum challenge. Prime-boost administration of the pcDNA-A2-CPA-CPB(-CTE) delivered by either electroporation or cSLN formulation protects BALB/c mice against L. infantum challenge and that protective immunity is associated with high levels of IFN-γ and lower levels of IL-10 production, leading to a strong Th1 immune response. At all time points, the ratio of IFN-γ: IL-10 induced upon restimulation with rA2-rCPA-rCPB and F/T antigens was significantly higher in vaccinated animals. Moreover, Th2-efficient protection was elicited through a high humoral immune response. Nitric oxide production, parasite burden and histopathological analysis were also in concordance with other findings. Overall, these data indicate that similar to the electroporation delivery system, cSLNs as a nanoscale vehicle of Leishmania antigens could improve immune response, hence indicating the promise of these strategies against visceral leishmaniasis.
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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".