Marked Increase in Macrophage Migration Inhibitory Factor Synthesis and Secretion in Human Endometrial Cells in Response to Human Chorionic Gonadotropin Hormone
Bibliographic record
Abstract
Originally identified for its capacity to inhibit the random migration of macrophages in vitro, macrophage migration inhibitory factor (MIF) is now recognized as a multifunctional cytokine that modulates the immune response and acts as a growth and angiogenic factor. Recent studies showed that MIF is expressed in the human endometrium across the menstrual cycle as well as in chorionic villi from first-trimester human placenta, which suggests an involvement of MIF in reproduction. Herein, we report that human chorionic gonadotropin (hCG), a glycoprotein hormone that plays a critical role in the initiation and maintenance of pregnancy, markedly stimulates MIF expression in endometrial stromal cells. Cell treatment with hCG resulted in a dose-dependent increase in MIF protein secretion and mRNA steady-state levels, as shown by immunocytochemistry, ELISA, and RT-PCR. Assessment of MIF mRNA half-life showed that hCG treatment had no significant effect on MIF mRNA stability (P = 0.08). However, nuclear transcription assays (run-on) revealed that hCG acts predominantly by up-regulating MIF gene transcription. These data clearly indicate that MIF can mediate hCG effects on the human endometrium and, in view of the immunomodulatory and angiogenic properties of MIF, reveal a new mechanism by which hCG sustains human pregnancy and promotes embryonic growth.
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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".