Hyperproduction of Proinflammatory Cytokines by WSX-1-Deficient NKT Cells in Concanavalin A-Induced Hepatitis
Bibliographic record
Abstract
Administration of Con A induces liver injury that is considered to be an experimental model for human autoimmune or viral hepatitis, where immunopathology plays roles mediated by activated lymphocytes, especially NK1.1+ CD3+ NKT cells, and inflammatory cytokines, including IFN-gamma and IL-4. In the present study we investigated the role of WSX-1, a component of IL-27R, in Con A-induced hepatitis by taking advantage of WSX-1 knockout mice. WSX-1-deficient mice were more susceptible to Con A treatment than wild-type mice, showing serum alanine aminotransferase elevation and massive necrosis in the liver. Although the development of NKT cells appeared normal in WSX-1 knockout mice, purified NKT cells from the knockout mice produced more IFN-gamma and IL-4 than those from wild-type mice in response to stimulation with Con A both in vitro and in vivo. In addition, hyperproduction of proinflammatory cytokines, including IL-1, IL-6, and TNF-alpha, was observed in the knockout mice after Con A administration. These data revealed a novel role for WSX-1 as an inhibitory regulator of cytokine production and inflammation in Con A-induced hepatitis.
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) | 0.001 | 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 | 0.000 |
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".