Identification of Macrophage Migration Inhibitory Factor as a Potent Endothelial Cell Growth-Promoting Agent Released by Ectopic Human Endometrial Cells <sup>1</sup>
Bibliographic record
Abstract
The growth of endometrial cells in ectopic locations (endometriosis) is dependent on the establishment of an adequate blood supply. Neovascularization (angiogenesis) is therefore a vital step toward the progression of this disease. We first revealed the presence of a potent mitogenic activity for human endothelial cells in the culture medium of immortalized human endometriotic cells. The activity, measured by the level of [(3)H]-thymidine incorporation into the DNA of human coronary artery endothelial cells, was then purified by anion exchange high-performance liquid chromatography. Electrophoretic analysis of one of the bioactive fractions that markedly enhanced endothelial cell proliferation showed three distinct bands with apparent molecular masses of 15.8, 12.6, and 6.5 kDa. N-terminal microsequencing of an internal peptide from the 12. 6-kDa protein showed 100% homology with human macrophage migration inhibitory factor (MIF). The protein was positively identified as MIF by Western blot analysis using a specific anti-MIF antibody. Anti-MIF antibody inhibited the bioactivity found in the evaluated fraction and the conditioned medium of primary endometriotic cell cultures, and commercial recombinant human MIF displayed a high mitogenic activity for endothelial cells. Our findings reveal that MIF is released by endometriotic cells and acts as a potent mitogenic factor for human endothelial cells in vitro. This may have a considerable interest, in view of the crucial role of angiogenesis in ectopic endometrial cell growth and activity and in numerous tissues undergoing dynamic physiological changes, such as 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 | 0.000 |
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".