Macrophage Migration Inhibitory Factor Is Markedly Expressed in Active and Early-Stage Endometriotic Lesions
Bibliographic record
Abstract
The establishment of a new vascular supply is essential for the survival of endometrial tissue and its development in ectopic locations. We have previously shown that ectopic endometrial cells release an important mitogenic activity for human endothelial cells and identified macrophage migration inhibitory factor (MIF) as one of the principal bioactive molecules involved in endothelial cell proliferation. In the present study, immunohistochemical and dual immunofluorescence analyses showed that MIF is effectively expressed by endometriotic tissue, particularly in the glands, and identified endothelial cells, macrophages, and T lymphocytes as cells markedly expressing MIF in the stroma. Western blot analysis showed a single 12.5-kDa band corresponding to the known mol wt of the molecule. The highest concentrations of MIF protein in endometriotic tissue, as measured by ELISA, were found in flame-like red endometriotic lesions, compared with typical black-bluish (P < 0.01) or with white lesions (P < 0.01). Interestingly, MIF displayed a marked expression in lesions from the initial stage of endometriosis (stage I). Semiquantitative RT-PCR analysis of MIF mRNA levels in the same endometriotic tissues showed a pattern of expression comparable with that of the protein. In view of its potent proinflammatory and angiogenic properties, local production of MIF within endometrial implants, particularly in those that are highly vascularized and representing the earliest and most active forms of the disease, make plausible the involvement of this factor in the local immunoinflammatory process observed in endometriosis and the initial steps of endometriotic tissue growth and development.
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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 | 0.001 | 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".