Tumour Necrosis Factor-α Regulates Human Eosinophil Apoptosis via Ligation of TNF-Receptor 1 and Balance between NF-κB and AP-1
Bibliographic record
Abstract
Eosinophils play a central role in asthma. The present study was performed to investigate the effect of tumour necrosis factor-α (TNF-α) on longevity of isolated human eosinophils. In contrast to Fas, TNF-α inhibited eosinophil apoptosis as evidenced by a combination of flow cytometry, DNA fragmentation assay and morphological analyses. The effect of TNF-α on eosinophil apoptosis was reversed by a TNF-α neutralising antibody. The anti-apoptotic effect of TNF-α was not due to autocrine release of known survival-prolonging cytokines interleukins 3 and 5 or granulocyte-macrophage-colony-stimulating factor as their neutralisation did not affect the effect of TNF-α. The anti-apoptotic signal was mediated mainly by the TNF-receptor 1. TNF-α induced phosphorylation and degradation of IκB and an increase in NF-κB DNA-binding activity. The survival-prolonging effect of TNF-α was reversed by inhibitors of NF-κB pyrrolidinedithiocarbamate and gliotoxin and by an inhibitor of IκB kinase, BMS-345541. TNF-α induced also an increase in AP-1 DNA-binding activity and the antiapoptotic effect of TNF-α was potentiated by inhibitors of AP-1, SR 11302 and tanshinone IIA and by an inhibitor of c-jun-N-terminal kinase, SP600125, which is an upstream kinase activating AP-1. Our results thus suggest that TNF-α delays human eosinophil apoptosis via TNF-receptor 1 and the resulting changes in longevity depend on yin-yang balance between activation of NF-κB and AP-1.
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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 | 0.000 |
| 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".