The Dysregulated IL-23/TH17 Axis in Endometriosis Pathophysiology
Bibliographic record
Abstract
Endometriosis is a chronic inflammatory disease in which endometrial-like tissue grows ectopically, resulting in pelvic pain and infertility. IL-23 is a key contributor in the development and differentiation of TH17 cells, driving TH17 cells toward a pathogenic profile. In a variety of inflammatory and autoimmune disorders, TH17 cells secrete proinflammatory cytokines, including IL-17, contributing to disease pathophysiology. Our studies and others have implicated IL-17 and TH17 cell dysregulation in endometriosis, which is associated with disease severity. In this article, we address whether IL-23-driven TH17 cells contribute to cardinal features of lesion proliferation, vascularization, and inflammation in endometriosis using patient samples, representative cell lines, and our established mouse model of endometriosis. The results indicated dysregulated expression of key genes in the IL-23/TH17 axis in patient ectopic and eutopic endometrial samples and increased IL-23 protein in patient plasma compared with controls. In vitro studies using primary human TH cells determined that rIL-23 mixture treatment increased pathogenic TH17 cell frequency. Similarly, rIL-23 treatment of cell lines (12Z cells, EECCs, HUVECs, and hESCs) representative of the endometriotic lesion microenvironment increased cytokines and growth factors, which play a role in lesion establishment and maintenance. In a syngeneic mouse model of endometriosis, rIL-23 treatment altered numbers of myeloid and T cell subsets in peritoneal fluid and increased giant cells within the lesion. Lesions from rIL-23-treated mice did not reveal significant alterations in proliferation/vascularization, although trends of increased proliferation and vascularization were observed. Collectively, these findings provide insights into the impact of the IL-23/TH17 axis on local immune dysfunction and broadly on endometriosis pathophysiology.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".