Expression of serum- and glucocorticoid-regulated kinase (<i>sgk</i>) mRNA is up-regulated by GM-CSF and other proinflammatory mediators in human granulocytes
Bibliographic record
Abstract
Stimulation of human peripheral blood granulocytes with the proinflammatory cytokine, granulocyte-macrophage colony-stimulating factor (GM-CSF), increases incorporation of [3H]uridine into RNA. We investigated the nature of the RNA synthesized under these conditions. Using transcription inhibitors, gel electrophoresis, and high-salt precipitation, it was concluded that as much as 90% of this radiolabeled RNA represents polymerase II transcripts. Differential display reverse transcription-polymerase chain reaction was used to identify and clone GM-CSF-responsive mRNAs. Serum- and glucocorticoid-regulated kinase (sgk) mRNA was identified that could be up-regulated 10- to 20-fold by > or =0. 1 ng/mL recombinant human GM-CSF. The 2.6-kb sgk mRNA was induced rapidly (within 30 min) by GM-CSF and remained at high levels for at least 12 h. Up-regulation was blocked completely by the transcription inhibitor, actinomycin D, but not by the translation inhibitor, cycloheximide, nor by the tyrosine kinase inhibitor, genistein. Up-regulation did not appear to be caused by enhanced mRNA stability. Other inflammatory mediators could also increase sgk mRNA levels (GM-CSF > > lipopolysaccharide > fMLP = tumor necrosis factor alpha). The function of sgk in granulocytes remains unknown.
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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".