T Cells Infiltrating Diseased Liver Express Ligands for the NKG2D Stress Surveillance System
Bibliographic record
Abstract
Abstract NK cells, which are highly enriched in the liver, are potent regulators of antiviral T cells and immunopathology in persistent viral infection. We investigated the role of the NKG2D axis in T cell/NK cell interactions in hepatitis B. Activated and hepatitis B virus (HBV)–specific T cells, particularly the CD4 fraction, expressed NKG2D ligands (NKG2DL), which were not found on T cells from healthy controls (p < 0.001). NKG2DL-expressing T cells were strikingly enriched within HBV-infected livers compared with the periphery or to healthy livers (p < 0.001). NKG2D+NK cells were also increased and preferentially activated in the HBV-infected liver (p < 0.001), in direct proportion to the percentage of MICA/B-expressing CD4 T cells colocated within freshly isolated liver tissue (p < 0.001). This suggests that NKG2DL induced on T cells within a diseased organ can calibrate NKG2D-dependent activation of local NK cells; furthermore, NKG2D blockade could rescue HBV-specific and MICA/B-expressing T cells from HBV-infected livers. To our knowledge, this is the first ex vivo demonstration that non-virally infected human T cells can express NKG2DL, with implications for stress surveillance by the large number of NKG2D-expressing NK cells sequestered in the liver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".