Obesity and PCOS radically alters the snRNA composition of follicular fluid extracellular vesicles
Bibliographic record
Abstract
Introduction: The ovarian follicle consists of the oocyte, somatic cells, and follicular fluid (FF). Proper signalling between these compartments is required for optimal folliculogenesis. The association between polycystic ovarian syndrome (PCOS) and extracellular vesicular small non-coding RNAs (snRNAs) signatures in follicular fluid (FF) and how this relates to adiposity is unknown. The purpose of this study was to determine whether FF extracellular vesicle (FFEV)-derived snRNAs are differentially expressed (DE) between PCOS and non-PCOS subjects; and if these differences are vesicle-specific and/or adiposity-dependent. Methods: FF and granulosa cells (GC) were collected from 35 patients matched by demographic and stimulation parameters. FFEVs were isolated and snRNA libraries were constructed, sequenced, and analyzed. Results: miRNAs were the most abundant biotype present, with specific enrichment in exosomes (EX), whereas in GCs long non-coding RNAs were the most abundant biotype. In obese PCOS vs. lean PCOS, pathway analysis revealed target genes involved in cell survival and apoptosis, leukocyte differentiation and migration, JAK/STAT, and MAPK signalling. In obese PCOS FFEVs were selectively enriched (FFEVs vs. GCs) for miRNAs targeting p53 signalling, cell survival and apoptosis, FOXO, Hippo, TNF, and MAPK signalling. Discussion: We provide comprehensive profiling of snRNAs in FFEVs and GCs of PCOS and non-PCOS patients, highlighting the effect of adiposity on these findings. We hypothesize that the selective packaging and release of miRNAs specifically targeting anti-apoptotic genes into the FF may be an attempt by the follicle to reduce the apoptotic pressure of the GCs and stave off premature apoptosis of the follicle observed in PCOS.
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 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 | 0.000 | 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) | 0.001 | 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".