Macrophage Migration Inhibitory Factor Is Involved in Ectopic Endometrial Tissue Growth and Peritoneal-Endometrial Tissue Interaction In Vivo: A Plausible Link to Endometriosis Development
Bibliographic record
Abstract
Pelvic inflammation is a hallmark of endometriosis pathogenesis and a major cause of the disease's symptoms. Abnormal immune and inflammatory changes may not only contribute to endometriosis-major symptoms, but also contribute to ectopic endometrial tissue growth and endometriosis development. A major pro-inflammatory factors found elevated in peritoneal fluid of women with endometriosis and to be overexpressed in peritoneal fluid macrophages and active, highly vascularized and early stage endometriotic lesions, macrophage migration inhibitory factor (MIF) appeared to induce angiogenic and inflammatory and estrogen producing phenotypes in endometriotic cells in vitro and to be a possible therapeutic target in vivo. Using a mouse model where MIF-knock out (KO) mice received intra-peritoneal injection of endometrial tissue from MIF-KO or syngeneic wild type (WT) mice and vice versa, our current study revealed that MIF genetic depletion resulted in a marked reduction ectopic endometrial tissue growth, a disrupted tissue structure and a significant down regulation of the expression of major inflammatory (cyclooxygenease-2), cell adhesion (αv and β3 integrins), survival (B-cell lymphoma-2) and angiogenic (vascular endothelial cell growth) factors relevant to endometriosis pathogenesis, whereas MIF add-back to MIF-KO mice significantly restored endometriosis-like lesions number and size. Interestingly, cross-experiments revealed that MIF presence in both endometrial and peritoneal host tissues is required for ectopic endometrial tissue growth and pointed to its involvement in endometrial-peritoneal interactions. This study provides compelling evidence for the role of MIF in endometriosis development and its possible interest for a targeted treatment of endometriosis.
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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".