Different subsets of natural killer T cells may vary in their roles in health and disease
Bibliographic record
Abstract
Natural killer T cells (NKT) can regulate innate and adaptive immune responses. Type I and type II NKT cell subsets recognize different lipid antigens presented by CD1d, an MHC class-I-like molecule. Most type I NKT cells express a semi-invariant T-cell receptor (TCR), but a major subset of type II NKT cells reactive to a self antigen sulphatide use an oligoclonal TCR. Whereas TCR-α dominates CD1d-lipid recognition by type I NKT cells, TCR-α and TCR-β contribute equally to CD1d-lipid recognition by type II NKT cells. These variable modes of NKT cell recognition of lipid-CD1d complexes activate a host of cytokine-dependent responses that can either exacerbate or protect from disease. Recent studies of chronic inflammatory and autoimmune diseases have led to a hypothesis that: (i) although type I NKT cells can promote pathogenic and regulatory responses, they are more frequently pathogenic, and (ii) type II NKT cells are predominantly inhibitory and protective from such responses and diseases. This review focuses on a further test of this hypothesis by the use of recently developed techniques, intravital imaging and mass cytometry, to analyse the molecular and cellular dynamics of type I and type II NKT cell antigen-presenting cell motility, interaction, activation and immunoregulation that promote immune responses leading to health versus disease outcomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".