Memory of mice and men: CD8<sup>+</sup> T‐cell cross‐reactivity and heterologous immunity
Bibliographic record
Abstract
The main functions of memory T cells are to provide protection upon re-exposure to a pathogen and to prevent the re-emergence of low-grade persistent pathogens. Memory T cells achieve these functions through their high frequency and elevated activation state, which lead to rapid responses upon antigenic challenge. The significance and characteristics of memory CD8+ T cells in viral infections have been studied extensively. In many of these studies of T-cell memory, experimental viral immunologists go to great lengths to assure that their animal colonies are free of endogenous pathogens in order to design reproducible experiments. These experimental results are then thought to provide the basis for our understanding of human immune responses to viruses. Although these findings can be enlightening, humans are not immunologically naïve, and they often have memory T-cell populations that can cross-react with and respond to a new infectious agent or cross-react with allo-antigens and influence the success of tissue transplantation. These cross-reactive T cells can become activated and modulate the immune response and outcome of subsequent heterologous infections, a phenomenon we have termed heterologous immunity. These large memory populations are also accommodated into a finite immune system, requiring that the host makes room for each new population of memory cell. It appears that memory cells are part of a continually evolving interactive network, where with each new infection there is an alteration in the frequencies, distributions, and activities of memory cells generated in response to previous infections and allo-antigens.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".