The Role of Invariant NKT in Autoimmune Liver Disease: Can Vitamin D Act as an Immunomodulator?
Bibliographic record
Abstract
Natural killer T (NKT) cells are a distinct lineage of T cells which express both the T cell receptor (TCR) and natural killer (NK) cell markers. Invariant NKT (iNKT) cells bear an invariant TCR and recognize a small variety of glycolipid antigens presented by CD1d (nonclassical MHC-I). CD1d-restricted iNKT cells are regulators of immune responses and produce cytokines that may be proinflammatory (such as interferon-gamma (IFN- γ )) or anti-inflammatory (such as IL-4). iNKT cells also appear to play a role in B cell regulation and antibody production. Alpha-galactosylceramide ( α -GalCer), a derivative of the marine sponge, is a potent stimulator of iNKT cells and has been proposed as a therapeutic iNKT cell activator. Invariant NKT cells have been implicated in the development and perpetuation of several autoimmune diseases such as multiple sclerosis and systemic lupus erythematosus (SLE). Animal models of SLE have shown abnormalities in iNKT cells numbers and function, and an inverse correlation between the frequency of NKT cells and IgG levels has also been observed. The role of iNKT cells in autoimmune liver disease (AiLD) has not been extensively studied. This review discusses the current data with regard to iNKT cells function in AiLD, in addition to providing an overview of iNKT cells function in other autoimmune conditions and animal models. We also discuss data regarding the immunomodulatory effects of vitamin D on iNKT cells, which may serve as a potential therapeutic target, given that deficiencies in vitamin D have been reported in various autoimmune disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".