Evaluation of Circulating Endometrial Cells as a Biomarker for Endometriosis.
Bibliographic record
Abstract
BACKGROUND: Circulating endometrial cells (CECs) have been reported to be present in the peripheral blood of women with endometriosis (EM), providing clear and specific evidence of the presence of ectopic lesions. In this study, we established a method with a high detection rate of CECs, assessed the diagnostic value of CECs for EM and compared with serum CA125, and proposed a hypothesis for the pathogenesis of EM from the new perspective of CECs. METHODS: The participants were enrolled prospectively from October 2015 to July 2016. The peripheral blood samples were collected from 59 participants, and the blood cells were isolated for immunofluorescence staining via microfluidic chips. The cells that were positive for vimentin/cytokeratin and estrogen/progesterone receptor and negative for CD45 were identified as CECs. The serum CA125 level was tested with electrochemiluminescence immunoassay. RESULTS: The detection rate of CECs reached 89.5% (17/19) in the EM group, which was significantly higher than that of the control group (15.0% [6/40], P < 0.001) and was independent of menstrual cycle phases. Furthermore, a positive CEC assay detected 4/5 cases of Stage I-II EM. In contrast, a positive CA125 test had limited value in detecting EM (13/19, 68.4%) and detected only one case of Stage I-II EM. CONCLUSION: CECs are promising biomarkers for EM with great potential for a noninvasive diagnostic assay.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".