The mRNA vaccine platform for veterinary species
Bibliographic record
Abstract
Vaccination has proven to be an effective means of controlling pathogens in animals. Since the introduction of veterinary vaccines in the 19th century, several generations of vaccines have been introduced. These vaccines have had a positive impact on global animal health and production. Despite, the success of veterinary vaccines, there are still some pathogens for which there are no effective vaccines available, such as African swine fever. Further, animal health is under the constant threat of emerging and re-emerging pathogens, some of which are zoonotic and can pose a threat to human health. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for new vaccine platforms that are safe and efficacious, but also importantly, are adaptable and can be modified rapidly to match the circulating pathogens. mRNA vaccines have been shown to be an effective vaccine platform against various viral and bacterial pathogens. This review will cover some of the recent advances in the field of mRNA vaccines for veterinary species. Moreover, various mRNA vaccines and their delivery methods, as well as their reported efficacy, will be discussed. Current limitations and future prospects of this vaccine platform in veterinary medicine will also be discussed.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.003 |
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".