A Toll-Like Receptor 3 Ligand Enhances Protective Effects of Vaccination Against Marek's Disease Virus and Hinders Tumor Development in Chickens
Bibliographic record
Abstract
Marek's disease (MD) is caused by Marek's disease virus (MDV). Various vaccines including herpesvirus of turkeys (HVT) have been used to control this disease. However, HVT is not able to completely protect against very virulent strains of MDV. The objective of this study was to determine whether a vaccination protocol consisting of HVT and a Toll-like receptor (TLR) ligand could enhance protective efficacy of vaccination against MD. Hence, chickens were immunized with HVT and subsequently treated with synthetic double-stranded RNA polyriboinosinic polyribocytidylic [poly(I:C)], a TLR3 ligand, before or after being infected with a very virulent strain of MDV. Among the groups that were HVT-vaccinated and challenged with MDV, the lowest incidence of tumors was observed in the group that received poly(I:C) before and after MDV infection. Moreover, the groups that received a single poly(I:C) treatment either before or after MDV infection were better protected against MD tumors compared to the group that only received HVT. No association was observed between viral load, as determined by MDV genome copy number, and the reduction in tumor formation. Overall, the results presented here indicate that poly(I:C) treatment, especially when it is administered prior to and after HVT vaccination, enhances the efficacy of HVT vaccine and improves protection against MDV.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".