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 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".