Virus-infected human mast cells enhance Natural Killer cell functions (VIR1P.1160)
Bibliographic record
Abstract
Abstract Mast cells are widely distributed throughout vascularised tissues. At mucosal surfaces they play important roles in host defence. Human mast cells produce chemokines, such as CXCL8, that selectively recruit Natural Killer (NK) cells in models of viral infection. However, the ability of mast cells to enhance NK cell effector functions has not been previously examined. Primary human mast cells were infected with reovirus and derived mast cell products were used for stimulation of human NK cells. Mast cell products induced expression of the activation marker CD69 (n=7; p<0.001), the cytotoxicity-related genes PRF1 (n=6; p<0.01) and TIA-1 (n=5; p<0.05), and enhanced NK cell cytotoxic activity against K562 cells (n=6; p<0.01). Interferon-γ was produced by NK cells treated with virus-induced mast cell mediators in the presence of IL-18 (n=7; p<0.001). In vivo, reovirus-infected human mast cells induced a 7-fold increase in recruitment and activated murine NK cells (42.3% CD69+ vs 5.8% CD69+ cells) in comparison to uninfected mast cells in a subcutaneous matrigel model (n=9; p<0.05). Soluble products of reovirus-infected mast cells included IL-10, type I and type III interferons. Blockade of type I interferon receptors revealed that they were important for mast cell-mediated NK cell activation. Our data define a novel mast cell-NK cell immune axis in human host defense against viral infection, which could be rapidly induced prior to the generation of acquired immunity.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".