The Case for Exploiting Cross-Species Epitopes in Malaria Vaccine Design
Bibliographic record
Abstract
The infection dynamics between different species of Plasmodium that infect the same human host can both suppress and exacerbate disease. This could arise from inter-parasite interactions, such as competition, from immune regulation, or both. The occurrence of protective, cross-species (heterologous) immunity is an unlikely event, especially considering that strain-transcending immunity within a species is only partial despite lifelong exposure to that species. Here we review the literature in humans and animal models to identify the contexts where heterologous immunity can arise, and which antigens may be involved. From the perspective of vaccine design, understanding the mechanisms by which exposure to an antigen from one species can elicit a protective response to another species offers an alternative strategy to conventional approaches that focus on immunodominant antigens within a single species. The underlying hypothesis is that certain epitopes are conserved across evolution, in sequence or in structure, and shared in antigens from different species. Vaccines that focus on conserved epitopes may overcome the challenges posed by polymorphic immunodominant antigens; but to uncover these epitopes requires approaches that consider the evolutionary history of protein families across species. The key question for vaccinologists will be whether vaccines that express these epitopes can elicit immune responses that are functional and contribute to protection against Plasmodium parasites.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".