Understanding the role of IL-33 in endometriosis associated inflammation and pathology
Bibliographic record
Abstract
Abstract Introduction Endometriosis (EM) is a chronic inflammatory disease categorized by the growth of endometrial tissue on the ovaries, peritoneal wall and other internal organs. Despite it’s prevalence (176 million women worldwide), the etiology is unknown. Currently, there is a significant knowledge gap regarding how the immune microenvironment contributes to the progression of EM (specifically inflammation, pain and fibrosis). We have shown that interleukin(IL)-33 is produced by EM lesions and drives pathology in a mouse model of EM. We are now investigating, mechanistically, how IL-33 contributes to EM and whether IL-33 neutralization could alleviate the pathology. Methods Female C57BL/6 mice were induced with EM and were treated with PBS (n=10) or IL-33 (n=10) every other day. After 2 weeks, the mice were euthanized and plasma, peritoneal fluid (PF) and EM lesions were collected. Cytokines in the plasma and PF were analyzed using a multiplex array. Immune cell populations in the PF were evaluated using CyTOF. Finally, sectioned lesions were stained for markers of innervation, proliferation and fibrosis. To establish cause and effect, mice were induced with EM and treated with IL-33+anti-IL-33 antibody (n=10) or IL-33+isotype control (n=10). Results Mice treated with IL-33 had elevated cytokines (e.g. IL-5) in the plasma and PF and EM lesions exhibited drastic changes in morphology. Additionally, both innate and adaptive immune cells were altered in the PF of IL- 33 treated mice. Neutralizing IL-33 reduced inflammation and immune cell recruitment. Conclusions Overall, these results show that IL-33 drives hallmark pathologies of endometriosis and neutralization of IL-33 could provide a novel therapeutic target for EM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".