T cell receptor sharing by cytotoxic T lymphocytes facilitates efficient virus control
Bibliographic record
Abstract
A remarkable feature of the adaptive immune system is the speed at which small numbers of antigen-specific lymphocytes can mediate a successful immune response. Rapid expansion of T and B lymphocyte clones that have receptors specific for a particular antigen is one of the primary means by which a swift response is generated. Although much of this clonal expansion is caused by the division of antigen-specific cells, here we demonstrate an additional mechanism by which the pool of effector T cells against a viral infection can quickly enlarge. Our data show that virus-specific CD8+ cytotoxic T lymphocytes (CTL) can transfer their T cell receptors (TCR) to recipient CTL of an unrelated specificity that, as a consequence, gain the antigen specificity of the donor T cell. This process occurs within minutes via membrane exchange and results in the recipient CTL acquiring the ability to recognize and eliminate cells targeted by the donor TCR, while still retaining the antigen specificity of its own TCR. Such receptor sharing allows rapid, proliferation-independent expansion of virus-specific T cell clones of low frequency and plays a highly significant antiviral role that can protect the host from an otherwise lethal infection.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".