Invariant NKT Cells: Regulation and Function during Viral Infection
Bibliographic record
Abstract
Natural killer T cells (NKT cells) represent a subset of T lymphocytes that express natural killer (NK) cell surface markers. A subset of NKT cells, termed invariant NKT cells (iNKT), express a highly restricted T cell receptor (TCR) and respond to CD1d-restricted lipid ligands. iNKT cells are now appreciated to play an important role in linking innate and adaptive immune responses and have been implicated in infectious disease, allergy, asthma, autoimmunity, and tumor surveillance. Advances in iNKT identification and purification have allowed for the detailed study of iNKT activity in both humans and mice during a variety of chronic and acute infections. Comparison of iNKT function between non-pathogenic simian immunodeficiency virus (SIV) infection models and chronic HIV-infected patients implies a role for iNKT activity in controlling immune activation. In vitro studies of influenza infection have revealed novel effector functions of iNKT cells including IL-22 production and modulation of myeloid-derived suppressor cells, but ex vivo characterization of human iNKT cells during influenza infection are lacking. Similarly, as recent evidence suggests iNKT involvement in dengue virus pathogenesis, iNKT cells may modulate responses to a number of emerging pathogens. This Review will summarize current knowledge of iNKT involvement in responses to viral infections in both human and mouse models and will identify critical gaps in knowledge and opportunities for future study. We will also highlight recent efforts to harness iNKT ligands as vaccine adjuvants capable of improving vaccination-induced cellular immune responses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".