Memory of mice and men: CD8<sup>+</sup> T‐cell cross‐reactivity and heterologous immunity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it