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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".