Pursuing effective vaccines against cattle diseases caused by apicomplexan protozoa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 it