The effects of in ovo administration of encapsulated Toll-like receptor 21 ligand as an adjuvant with Marek’s disease vaccine
Bibliographic record
Abstract
Marek's Disease Virus (MDV) is the causative agent of a lymphoproliferative disease, Marek's disease (MD) in chickens. MD is only controlled by mass vaccination; however, immunity induced by MD vaccines is unable to prevent MDV replication and transmission. The herpesvirus of turkey (HVT) vaccine is one of the most widely used MD vaccines in poultry industry. Vaccines can be adjuvanted with Toll-like receptor ligands (TLR-Ls) to enhance their efficacy. In this study, we examined whether combining TLR-Ls with HVT can boost host immunity against MD and improve its efficacy. Results demonstrated that HVT alone or HVT combined with encapsulated CpG-ODN partially protected chickens from tumor incidence and reduced virus replication compared to the control group. However, encapsulated CpG-ODN only moderately, but not significantly, improved HVT efficacy and reduced tumor incidence from 53% to 33%. Further investigation of cytokine gene profiles in spleen and bursa of Fabricius revealed an inverse association between interleukin (IL)-10 and IL-18 expression and protection conferred by different treatments. In addition, the results of this study raise the possibility that interferon (IFN)-β and IFN-γ induced by the treatments may exert anti-viral responses against MDV replication in the bursa of Fabricius at early stage of MDV infection in chickens.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".