CD80 and CD86 Control Antiviral CD8 <sup>+</sup> T-Cell Function and Immune Surveillance of Murine Gammaherpesvirus 68
Bibliographic record
Abstract
The interactions between CD80 and CD86 on antigen-presenting cells and CD28 on T cells serve as an important costimulatory signal in the activation of T cells. Although the simplistic two-signal hypothesis has been challenged in recent years by the identification of different costimulators, this classical pathway has been shown to significantly impact antiviral humoral and cellular immune responses. How the CD80/CD86-CD28 pathway affects the control of chronic or latent infections has been less well characterized. In this study, we investigated its role in antiviral immune responses against murine gammaherpesvirus 68 (MHV-68) and immune surveillance using CD80/CD86(-/-) mice. In the absence of CD80/CD86, primary antiviral CD8(+) T-cell responses and the induction of neutralizing antibodies were severely impaired. During long-term immune surveillance, the virus-specific CD8(+) T cells were impaired in IFN-gamma production and secondary expansion and exhibited an altered phenotype. Surprisingly, a low level of viral reactivation in the lung was observed, and this effect was independent of CD28 and CTLA-4. Thus, CD80 and CD86, signaling through CD28 and possibly another unidentified receptor, are required for optimal immune surveillance and antiviral immune responses to murine gammaherpesvirus.
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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".