Extracellular vesicles from endometriosis patients are characterized by a unique miRNA-lncRNA signature
Bibliographic record
Abstract
With multifactorial etiologies, combined with disease heterogeneity and a lack of suitable diagnostic markers and therapy, endometriosis remains a major reproductive health challenge. Extracellular vesicles (EVs) have emerged as major contributors of disease progression in several conditions, including a variety of cancers; however, their role in endometriosis pathophysiology has remained elusive. Using next-generation sequencing of EVs obtained from endometriosis patient tissues and plasma samples compared with controls, we have documented that patient EVs carry unique signatures of miRNAs and long noncoding RNAs (lncRNAs) reflecting their contribution to disease pathophysiology. Mass spectrophotometry-based proteomic analysis of EVs from patient plasma and peritoneal fluid further revealed enrichment of specific pathways, as well as altered immune and metabolic processes. Functional studies in endometriotic epithelial and endothelial cell lines using EVs from patient plasma and controls clearly indicate autocrine uptake and paracrine cell proliferative roles, suggestive of their involvement in endometriosis. Multiplex cytokine analysis of cell supernatants in response to patient and control plasma-derived EVs indicate robust signatures of important inflammatory and angiogenic cytokines known to be involved in disease progression. Collectively, these findings suggest that endometriosis-associated EVs carry unique cargo and contribute to disease pathophysiology by influencing inflammation, angiogenesis, and proliferation within the endometriotic lesion microenvironment.
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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 | 0.001 |
| 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 | 0.001 |
| 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".