Control of In Vivo Collateral Damage Generated by T Cell Immunity
Bibliographic record
Abstract
An ongoing dilemma faced during an immune response is generating an effective, often proinflammatory response to eliminate pathogens and/or infected cells while also minimizing collateral damage to adjacent noninfected tissues. The factors limiting bystander cell injury during an Ag-specific immune response in vivo are largely unknown. In this study, using an in vivo model of islet transplants in TCR transgenic mice, we show that both CD4 and CD8 T cells do have the capacity to inflict adjacent tissue damage and that this injury is greatly enhanced in sensitized hosts. CD4 T cell-mediated killing of specific and bystander cells occurred via different mechanisms. Unlike specific target cell killing, CD4-mediated bystander injury required tissue Fas expression and was inhibited with anti-IFN-γ Ab treatment in vivo. Moreover, bystander cell injury was not entirely nonspecific but rather required, in naive recipients, that the MHC allele expressed by the bystanders was self. Importantly, the coinhibitor programmed death-1 plays an important role in restraining bystander cell injury mediated either by defined TCR transgenic T cells or by polyclonal T cell populations. Thus, the differential requirements for specific versus bystander cell injury suggest that there are opportunities for inhibiting immune pathology without compromising Ag-specific immunity in vivo.
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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 | 0.001 |
| 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".