Tumour necrosis factor-α up-regulates macrophage migration inhibitory factor expression in endometrial stromal cells via the nuclear transcription factor NF-κB
Bibliographic record
Abstract
BACKGROUND: A series of controlled changes including proliferation, secretion and menstrual shedding occur in the human endometrium during every normal menstrual cycle. Macrophage migration inhibitory factor (MIF), a multifunctional cytokine with numerous proinflammatory, immunomodulatory and angiogenic properties, appears to be expressed in the human endometrium and to follow a regulated cycle phase-dependent expression, but the mechanisms underlying endometrial MIF expression remain to be fully elucidated. METHODS AND RESULTS: Results from enzyme-linked immunosorbent assay (ELISA) demonstrated a significant dose- and time-dependent increase in MIF secretion by human endometrial cells in response to tumour necrosis factor-alpha (TNF-alpha) (0.1-100 ng/ml). This increase was also observed at the mRNA level as shown by reverse transcription (RT)-PCR. Curcumin (10(-8) mol/l), a known nuclear factor (NF)-kappaB inhibitor, inhibited the TNF-alpha-induced pIkappaB phosphorylation as shown by western blotting, NF-kappaB translocation into the nucleus as shown by electrophoretic mobility shift assay, and MIF synthesis and secretion as measured by ELISA and RT-PCR. The expression of a dominant-negative NF-kappaB inhibitor (IkappaB) significantly decreased the TNF-alpha-induced MIF promoter activity as analysed by transient cell transfection. CONCLUSIONS: These results indicate clearly that TNF-alpha up-regulates the expression of MIF in endometrial stromal cells. This took place possibly through NF-kappaB activation, and may play an important role in the physiology of the human endometrium.
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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 | 0.001 |
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".