An investigation of the IL-23/Th17 axis and transcriptomic profiles of Th1, Th1/17, and Th17 cells in endometriosis patients as compared to controls 3189
Bibliographic record
Abstract
Abstract Description Endometriosis (EM) is an inflammatory disease driven by immune dysfunction. IL-23 is a key contributor driving IL-17-producing T-helper (Th)17 cells towards a pathogenic phenotype. IL-17 and Th17 cells are elevated and linked to EM severity. Though, mechanisms by which IL-23/mediators contribute to pathogenic Th17 profile and exacerbate EM is unknown. We seek to establish transcriptomic signatures of Th1, Th1/17, and Th17 cell subsets isolated from peripheral blood of EM patients and controls. Bulk RNA sequencing is in progress to investigate transcriptional profiles of these cell subsets. Results will be integrated with RNA transcriptome from patient eutopic and ectopic tissues. Using high-parameter flow cytometry, expression of markers distinguishing pathogenic and non-pathogenic Th17 cells will be assessed in patient peritoneal fluid (PF). Relevant cytokines in IL-23/Th17 axis were measured via multiplex cytokine array in patient PF, plasma (patients and controls), and protein extracts isolated from patient tissues. Preliminary results reveal significant dysregulation of IL-23/Th17 axis in EM. RNA sequencing and associated analyses will reveal interplay between Th1 and Th17 cell subsets and integration with immune phenotyping of PF will reflect local immune microenvironment, directly influencing EM lesion survival. This work may provide a promising therapeutic avenue to reduce the burden of EM, as IL-23 therapeutics are currently in use for various inflammatory diseases. Funding Sources This research is supported with funds from the Canadian Institutes of Health Research (CIHR 394570, CT and CIHR CGS-D 187578, DS). Topic Categories Immune Mechanisms of Human Disease (HUM)
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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) | 0.002 | 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".