Pursuing effective vaccines against cattle diseases caused by apicomplexan protozoa
Bibliographic record
Abstract
Abstract Apicomplexan parasites are responsible for important livestock diseases that affect the production of much needed protein resources, and those transmissible to humans pose a public health risk. Vaccines, recognized as a cost-effective and environmentally friendly method for the prevention of infectious diseases in livestock, can avert losses in food production and decrease the exposure of humans to zoonotic pathogens. This review focuses on the need for and advances in vaccine development against the apicomplexan parasites Theileria spp., Babesia spp., Toxoplasma gondii , Neospora caninum , Eimeria spp., Besnoitia spp., Sarcocystis spp., and Cryptosporidium parvum . Together, the effect of these parasites on the cattle industry worldwide causes an enormous burden, yet they remain poorly controlled and very few effective and practical vaccines against them are available. Vaccine development is hampered by our scarce and limited knowledge of the biology and mechanisms of pathogenesis of these microorganisms, and the absence of correlates of host immune protection. More studies focused on these aspects as well as on the identification of parasite vulnerabilities that can be exploited for vaccine design are needed. Novel “omics” and gene editing approaches in understanding complex parasite biology together with advances in vaccinology will facilitate the development of effective, sustainable, and practical vaccines against cattle diseases caused by apicomplexan parasites. Such vaccines will help prevent animal and human diseases and allow production of enough animal protein to feed the growing human population in the twenty-first century and beyond.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".