Recent Advances in the Development of Adenovirus-Vectored Vaccines for Parasitic Infections
Bibliographic record
Abstract
Vaccines against parasites have lagged centuries behind those against viral and bacterial infections, despite the devastating morbidity and widespread effects of parasitic diseases across the globe. One of the greatest hurdles to parasite vaccine development has been the lack of vaccine strategies able to elicit the complex and multifaceted immune responses needed to abrogate parasitic persistence. Viral vectors, especially adenovirus (AdV) vectors, have emerged as a potential solution for complex disease targets, including HIV, tuberculosis, and parasitic diseases, to name a few. AdVs are highly immunogenic and are uniquely able to drive CD8+ T cell responses, which are known to be correlates of immunity in infections with most protozoan and some helminthic parasites. This review presents recent developments in AdV-vectored vaccines targeting five major human parasitic diseases: malaria, Chagas disease, schistosomiasis, leishmaniasis, and toxoplasmosis. Many AdV-vectored vaccines have been developed for these diseases, utilizing a wide variety of vectors, antigens, and modes of delivery. AdV-vectored vaccines are a promising approach for the historically challenging target of human parasitic diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".