Cytokine Gene Expression in Splenic CD4 <sup>+</sup> and CD8 <sup>+</sup> T-Cell Subsets of Chickens Infected with Marek's Disease Virus
Bibliographic record
Abstract
Specific-pathogen free chickens were infected with the RB1B strain of Marek's disease virus (MDV) and T cells from the spleens of infected as well as age-matched controls were fractionated by flow cytometry at 4, 10, and 21 days post-infection (d.p.i.). Real-time quantitative reverse transcription PCR was used to assess the amount of cytokine transcripts as well as viral genes meq and glycoprotein B (gB). There was an increase in the number of CD4(+) T cells, as well as a significant increase in the expression of the viral meq gene in CD4(+) T cells, which coincided with the presence of tumors in various organs of infected birds. It was also observed that there was a significant upregulation in the amount of the gene expression of interferon (IFN)-gamma, interleukin (IL)-18, and IL-6 at 4 and 21 d.p.i. in CD4(+) and CD8(+) T-cell subsets. The expression of IL-10 was upregulated as well. The outcome of the cytokine milieu inclined towards the induction of a type I immune response at 4 and 21 d.p.i. Our study indicates that MDV-associated cytokine profiles vary in CD4(+) and CD8(+) T-cell subsets, and that cytokines including IFN-gamma, IL-18, IL-6, and IL-10 may play a role in the elicitation of an immune response to MDV.
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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".