Towards developing multistrain PEDV vaccines: Integrating basic concepts and SARS-CoV-2 pan-sarbecovirus strategies
Bibliographic record
Abstract
Porcine epidemic diarrhea virus (PEDV) is a major pathogen impacting the global pig industry, with outbreaks causing significant financial losses. The genetic variability of PEDV has posed challenges for vaccine development since its identification in the 1970s, a problem that intensified with its global emergence in the 2010s. Since current vaccines provide limited cross-protection against PEDV strains, and the development of multistrain PEDV vaccines remains an underexplored area of research, there is an urgent need for improved vaccine solutions. The rapid development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines and ongoing pan-sarbecovirus vaccine research, have demonstrated the potential of next-generation vaccine platforms and novel antigen design strategies. These advancements offer valuable insights for the development of multistrain PEDV vaccines. This review summarizes key aspects of PEDV virology and explores multistrain vaccine development considering SARS-CoV-2 vaccine innovations, proposing a framework for developing next-generation PEDV vaccine solutions. • SARS-CoV-2 vaccine strategies offer guidance for developing multistrain PEDV vaccines. • Conserved and chimeric antigens present solutions for tackling PEDV genetic variability. • Next-generation platforms provide pathways for effective multistrain PEDV vaccine development.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 0.000 |
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 teacher head, 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".