Effect of Pre-Treatment with a Recombinant Chicken Interleukin-17A on Vaccine Induced Immunity against a Very Virulent Marek’s Disease Virus
Bibliographic record
Abstract
The host response to pathogenic microbes can lead to expression of interleukin (IL)-17, which has antimicrobial and anti-viral activity. However, relatively little is known about the basic biological role of chicken IL-17A against avian viruses, particularly against Marek’s disease virus (MDV). We demonstrate that, following MDV infection, upregulation of IL-17A mRNA and an increase in the frequency of IL-17A+ T cells in the spleen occur compared to control chickens. To elaborate on the role of chIL-17A in MD, the full-length chIL-17A coding sequence was cloned into a pCDNA3.1-V5/HIS TOPO plasmid. The effect of treatment with pcDNA:chIL-17A plasmid in combination with a vaccine (HVT) and very virulent(vv)MDV challenge or vvMDV infection was assessed. In combination with HVT vaccination, chickens that were inoculated with the pcDNA:chIL-17A plasmid had reduced tumor incidence compared to chickens that received the empty vector control or that were vaccinated only (66.6% in the HVT + empty vector group and 73.33% in HVT group versus 53.3% in the HVT + pcDNA:chIL-17A). Further analysis demonstrated that the chickens that received the HVT vaccine and/or plasmid expressing IL-17A had lower MDV-Meq transcripts in the spleen. In conclusion, chIL-17A can influence the immunity conferred by HVT vaccination against 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".