Bibliographic record
Abstract
The innate immune system represents a critical first line of host response to infectious, injurious and inflammatory insults. NKT cells (natural killer T-cells) are an important, but relatively poorly understood, component of the innate immune response. Moreover, NKT cells are enriched within the liver, suggesting that within the hepatic compartment NKT cells probably fulfil important roles in the modulation of the immune response to infection or injury. NKT cells are characterized by their rapid activation and secretion of large amounts of numerous types of cytokines, including those within the Th1-type, Th2-type and Th17-type groups, which in turn can interact with a multitude of other cell types within the liver. In addition, NKT cells are capable of participating in a wide array of effector functions with regards to other cell types via NKT cell-surface-molecule expression [e.g. FASL (FAS ligand) and CD40L (CD40 ligand)] and the release of mediators (e.g. perforin and granzyme) contained in cellular granules, which in turn can activate or destroy other cells (i.e. immune or parenchymal cells) within the liver. Given the huge scope of potential actions that can be mediated by NKT cells, it has become increasingly apparent that NKT cells may fulfil both beneficial (e.g. clearance of virally infected cells) and harmful (e.g. induction of autoimmunity) roles in the setting of liver disease. This review will outline the possible roles which may be played by NKT cells in the setting of specific liver diseases or conditions, and will discuss the NKT cell in the context of its role as either a 'friend' or a 'foe' with respect to the outcome of these liver 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".