The downside of human natural killer cell diversity in viral infection revealed by mass cytometry
Bibliographic record
Abstract
Antigen-specific receptor diversity is the hallmark of an immune cell and an asset in adaptive immunity (1). In contrast, natural killer (NK) diversity appears to be detrimental to its antiviral immune functions, as reported by Strauss-Albee and colleagues (2). Adaptive T and B cell diversity is generated by the rearrangement of their receptors during development, giving rise to a vast array of antigen specificities (3). It is estimated that the total diversity of T cell receptors (TCR) generated by somatic recombination in human T cells is in the order of 10 15 –10 20 sequences (4). Unlike these adaptive immune cells, innate immune cell diversity, including NK cells, is shaped by random assortment of germline-encoded cell surface receptors. NK cell receptors come in two flavours: activating and inhibitory. Unlike T cell activation, which is mediated by the engagement of TCR and co-stimulatory receptors, NK cell activation is determined by the net balance of signals from the engagement of activating and inhibitory receptors (5). Therefore, the diversity and cell surface expression of these receptors on a per-cell basis can have a profound influence on an NK cell’s function.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 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 teacher head, 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".