MicroRNA expression pattern differs depending on endometriosis lesion type†
Bibliographic record
Abstract
MicroRNA (miRNA), noncoding segments of RNA involved in post-transcriptional regulation of protein expression are differentially expressed in eutopic endometrium of women with and without endometriosis compared to endometriotic lesions. However, endometriotic lesion types are known to be biochemically distinct and therefore hypothesized that miRNAs are differentially expressed in endometriomas compared to peritoneal and deep-infiltrating lesions. Therefore, endometrial biopsies and ectopic implants from women (n = 38) undergoing laparoscopic surgery for chronic pelvic pain were collected. Samples of endometriomas, peritoneal or deep-infiltrating lesions were selected from our tissue bank for study participants who exclusively had only one lesion type noted on their surgical report. Quantitative real-time polymerase chain reaction for miR-9, miR-21, miR-424, miR-10a, miR-10b, and miR-204 was performed. miR-204 expression was significantly lower (P = 0.0016) in the eutopic endometrium of women with endometriosis compared to controls. Relative expression of miR-21, miR-424, and miR-10b differed significantly (P < 0.05) across endometriotic lesion types. Finally, all miRNAs isolated from endometriomas, peritoneal and deep-infiltrating lesions studied were differentially expressed compared to matched eutopic endometrium samples. We therefore conclude that miRNA expression in the eutopic endometrium from women with endometriosis differs from symptomatic controls. Moreover, miRNA expression pattern is dependent on the endometriotic lesion type studied. We suggest that identification of different miRNA expression patterns for endometriomas, peritoneal and deep-infiltrating lesions could contribute to individualized patient care for women with endometriosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".