Impaired NK cell cytotoxicity by high level of interferon-γ in concanavalin A-induced hepatitis
Bibliographic record
Abstract
Unlike T cells, the role of natural killer (NK) cells is not well documented in the concanavalin (ConA)- induced hepatitis model. This study aimed to investigate the regulatory effect of high levels of interferon-gamma (IFN-gamma) on NK cells in ConA-induced hepatitis. The cytotoxicities of NK cells from ConA-injected mice or NK cell lines (NK92 and NKL) were detected by the 4-h 51Cr release assay. Depletion of NK cells with AsGM1 antibody was used to assess the NK cell role in ConA-induced hepatitis. Expression of NK cell receptors and cytotoxic molecules was measured by reverse transcription-polymerase chain reaction. Twelve hours after ConA injection, serum IFN-gamma was significantly increased in wild mice, but not in severe combined immunodeficiency mice, and hepatic NK cells exerted impaired cytotoxicity against YAC-l cells in wild mice. Eight hours after NK cells were incubated in serum from ConA-treated mice, NK cell cytotoxicity was down-modulated and the effect was abolished by pretreatment with neutralizing serum IFN-gamma with specific antibody in vitro. A high concentration of IFN-gamma (> 1000 U/mL) inhibited the cytotoxicities of 2 NK cell lines in vitro, accompanied with down-regulation of NKG2D transcripts and up-regulation of NKG2A/B and KIR2DL transcripts. The inhibitive role of IFN-gamma was not seen in NKG2D ligand negative cells. These results suggest that NK cell cytotoxicity was inhibited by high levels of IFN-gamma in ConA-induced hepatitis, which may relate to the dispensable role of NK cells.
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 | 0.001 |
| 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".