NK Cell Memory in the absence of dominant TCR genes 4078
Bibliographic record
Abstract
Abstract Description The immune response, mediated by cells of the innate immune system, is generally considered non-specific. Conversely, cells of the adaptive immune system mediate their responses in an antigen-specific manner, forming immunological memory, allowing them to mediate long-lasting antigen-specific responses upon re-exposure to the same pathogen. Natural killer (NK) cells are among the founding members of innate lymphoid cells, specialized in the recognition and elimination of virally infected, tumour and abnormal cells. Recent studies have uncovered that NK cells also exhibit adaptive immune features similar to T and B cells, which holds promise for using the NK cell memory for the development of new cancer/viral immunotherapies. Research in our lab demonstrated that NK cells can elicit immunological memory in Rag 1-/- mice, which are devoid of adaptive T cells and B cells. However, there remains a need to explore whether this phenomenon persists in the absence of TCR genes. The ability of NK cells to mediate adaptive responses was evaluated by studying the contact hypersensitivity ear swelling response to chemical haptens and peptides in TCR β-/-δ-/- mice, in conjunction with NK cell depletion via anti-NK1.1. Preliminary results show memory responses can be attributed to NK cells in the absence of TCR genes and is antigen specific. A better understanding of these adaptive NK cell responses can be exploited in therapeutic/prophylactic treatments of cancer and viral infections. Funding Sources Funded by: Canadian Cancer Society Master’s Research Training Award Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)
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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) | 0.003 | 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".