Natural Killer T Cells within the Liver: Conductors of the Hepatic Immune Orchestra
Bibliographic record
Abstract
Natural killer T (NKT) cells are distinct innate immune T cells which play a key role in the pathogenesis of various immune-mediated liver diseases. NKT cells are traditionally defined as cells that co-express the T cell receptor (TCR) and certain natural killer cell surface markers (e.g. NK1.1 in some mouse strains). Based on CD1d restriction, they are broadly classified as invariant (iNKT) and noninvariant NKT cells. Both NKT cell types are abundant within the liver and, mainly through the study of animal models of immune-mediated liver injury, the role of NKT cells in the context of liver inflammation is becoming better understood. iNKT cells are the NKT cell subtype which has been most well characterized, and therefore this paper will focus mainly on iNKT cells. NKT cells recognize both host and microbial glycolipid antigens, presented by antigen-presenting cells in the context of the MHC class I- like molecule CD1d, via their T cell receptor. Upon activation, NKT cells rapidly release large quantities of T helper type 1 (Th1; e.g. IFN-gamma, TNF-alpha), Th2 (e.g. IL-4, IL-10), and Th17 (e.g. IL-17, IL-22) type cytokines, with diverse immunoregulatory and immunomodulatory effects. Moreover, through the release of these cytokines, NKT cells can drive subsequent downstream immune responses in tissues such as the liver, including the upregulation of adhesion molecule expression and increased chemokine production. By regulating immune cell adhesion pathways and chemokine secretion, NKT cells can direct both pro-inflammatory (e.g. Th1 cell) and anti-inflammatory (e.g. regulatory T cell) cell infiltration into the liver. Through this mechanism, NKT cells orchestrate a balance between pro- and anti-inflammatory responses within the liver during immune-mediated liver damage. This paper highlights the diverse roles played by iNKT cells as master regulators of immune-mediated liver injury.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".