T helper 17 axis and endometrial macrophage disruption in menstrual effluent provides potential insights into the pathogenesis of endometriosis
Bibliographic record
Abstract
Objective: To identify immune cells, cytokines, and immune cell transcriptome in the menstrual effluent (ME) of women with endometriosis compared with that of healthy donors.Design: Live immune cells were isolated from human ME samples and were analyzed by flow cytometry to identify various immune cell populations.Selected cytokines from the same patients were evaluated using multiplex cytokine analyses.The transcriptome of the immune cell population was subsequently profiled using NanoString nCounter's PanCancer Immune panel.Setting: Academic institution.Patient(s): Surgically confirmed endometriosis patients (n ¼ 14) and healthy fertile donors (n ¼ 19).Intervention(s): None.Main Outcome Measure(s): In-depth immune cell profiling of ME obtained from women with endometriosis compared with that of healthy donors.Result(s): ME analysis revealed that the number of T helper 17 (T H 17) cells was significantly lower in patients with endometriosis compared with that of healthy donors; the number of macrophages was also lower (P¼ .06) in the former.Multiplex cytokine analysis revealed significantly lower transforming growth factor a in the ME ''serum'' of patients with endometriosis.Transcriptomic analysis of CD45 þ cells revealed 47 differentially expressed genes, mainly associated with the T H 17 axis (IL10, IL23A, and IL6), as well as genes associated with macrophage signaling/activation (CD74, CD83, CXCL16, and CCL3). Conclusion(s):We demonstrate for the first time that the levels of T H 17 axis, macrophages, and transforming growth factor a were altered in the ME of women with endometriosis compared with that of healthy donors.These findings shed light on the potential immune pathways that could partly explain the pathogenesis and progression of endometriosis.Future large-scale studies on ME samples are warranted to exploit the use of these markers to study the pathogenesis of endometriosis.(
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".