A Marek’s Disease Virus Messenger RNA-Based Vaccine Modulates Local and Systemic Immune Responses in Chickens
Bibliographic record
Abstract
Marek's disease (MD), caused by the Marek's disease virus, is a lymphoproliferative disease in chickens that can be controlled by vaccination. However, the current vaccines can limit tumor growth and death but not virus replication and transmission. The present study aimed to evaluate host responses following intramuscular injection of an mRNA vaccine encoding gB and pp38 proteins of the MDV within the first 36 h. The vaccine was injected in low and high doses using prime and prime-boost strategies. The expression of type I and II interferons (IFNs), a panel of interferon-stimulated genes, and two key antiviral cytokines, IL-1β and IL-2, were measured in spleen and lungs after vaccination. The transcriptional analysis of the above genes showed significant increases in the expression of MDA5, Myd88, IFN-α, IFN-β, IFN-γ, IRF7, OAS, Mx1, and IL-2 in both the spleen and lungs within the first 36 h of immunization. Secondary immunization increased expression of all the above genes in the lungs. In contrast, only IFN-γ, MDA5, MyD88, Mx1, and OAS showed significant upregulation in the spleen after the secondary immunization. This study shows that two doses of the MDV mRNA vaccine encoding gB and pp38 antigens activate innate and adaptive responses and induce an antiviral state 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) | 0.000 | 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) | 0.001 | 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".