Long non-coding RNAs: novel players in the pathogenesis of polycystic ovary syndrome
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are a class of transcripts (>200 nucleotides) lacking protein-coding capacity. Based on the complex three-dimensional structure, lncRNAs are involved in many biological processes and can regulate the expression of target genes at chromatin modification, transcriptional and post-transcriptional levels. LncRNAs have been studied in multiple diseases but little is known about their role(s) in polycystic ovary syndrome (PCOS), the most common endocrinological disorder in reproductive-aged women around the world. In this review, we characterized and explored the potential mechanisms of lncRNAs in the pathogenesis of PCOS. We found that lncRNAs play a molecular role in PCOS mainly by functioning as the competitive endogenous RNA (ceRNA) and are significantly correlated with some clinical phenotypes. We summarized in detail regarding aberrant lncRNAs in different specimens of women with PCOS [i.e., granulosa cells (GCs), cumulus cells (CCs), follicular fluid (FF), peripheral blood] and various PCOS rodent models [i.e., dehydroepiandrosterone (DHEA) and letrozole induced models]. In clinical practice, detection of lncRNAs in serum might enable early diagnosis. Furthermore, new lncRNA-based classifications might be emerging as potent predictors of a particular phenotype in PCOS. Overall, we proposed new insights for the application of precision medicine approaches to the management of PCOS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".