Virus-Infected Human Mast Cells Enhance Natural Killer Cell Functions
Bibliographic record
Abstract
Mucosal surfaces are protected from infection by both structural and sentinel cells, such as mast cells. The mast cell's role in antiviral responses is poorly understood; however, they selectively recruit natural killer (NK) cells following infection. Here, the ability of virus-infected mast cells to enhance NK cell functions was examined. Cord blood-derived human mast cells infected with reovirus (Reo-CBMC) and subsequent mast cell products were used for the stimulation of human NK cells. NK cells upregulated the CD69 molecule and cytotoxicity-related genes, and demonstrated increased cytotoxic activity in response to Reo-CBMC soluble products. NK cell interferon (IFN)-γ production was also promoted in the presence of interleukin (IL)-18. In vivo, SCID mice injected with Reo-CBMC in a subcutaneous Matrigel model, could recruit and activate murine NK cells, a property not shared by normal human fibroblasts. Soluble products of Reo-CBMC included IL-10, TNF, type I and type III IFNs. Blockade of the type I IFN receptor abrogated NK cell activation. Furthermore, reovirus-infected mast cells expressed multiple IFN-α subtypes not observed in reovirus-infected fibroblasts or epithelial cells. Our data define an important mast cell IFN response, not shared by structural cells, and a subsequent novel mast cell-NK cell immune axis in human antiviral host defense.
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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 | 0.001 |
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".