Essential Role of the Adhesion Receptor LFA-1 for T Cell-Dependent Fulminant Hepatitis
Bibliographic record
Abstract
Viral hepatitis affects more than 2 billion people worldwide. In particular, no effective treatment exists to abrogate death and liver damage in fulminant hepatitis. Activation of T cells is an initial and critical event in the pathogenesis of liver damage in autoimmune and viral hepatitis. The precise molecular mechanisms that induce T cell-mediated hepatocyte injury remain largely unclear. In mice, T cell-dependent hepatitis and acute liver damage can be modeled using ConA. In this study, we examined the role of the adhesion receptor LFA-1 in ConA-induced acute hepatic damage using LFA-1(-/-) (CD11a) mice. Massive liver cell apoptosis and metabolic liver damage were observed in LFA-1(+/+) mice following ConA injection. By contrast, LFA-1(-/-) mice were completely resistant to ConA-induced hepatitis and none of the LFA-1(-/-) mice showed any hepatic damage. Whereas activated hepatic T cells remained in the liver in LFA-1(+/+) mice, activated T cells were rapidly cleared from the livers of LFA-1(-/-) mice. Mechanistically, T cells from LFA-1(-/-) mice showed markedly reduced cytotoxicity toward liver cells as a result of impaired, activation-dependent adhesion. Importantly, adoptive transfer of hepatic T cells from LFA-1(+/+) mice, but not from LFA-1(-/-) mice, sensitized LFA-1(-/-) mice to ConA-induced hepatitis. Thus, LFA-1 expression on T cells is necessary and sufficient for T cell-mediated liver damage in vivo. These results provide the first genetic evidence on an adhesion receptor, LFA-1, that has a crucial role in fulminant hepatitis. These genetic data identify LFA-1 as a potential key target for the treatment of T cell-mediated hepatitis and the prevention of liver damage.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".