Extracellular vesicle <scp>RNA</scp>s reflect placenta dysfunction and are a biomarker source for preterm labour
Bibliographic record
Abstract
Preterm birth (PTB) can lead to lifelong complications and challenges. Identifying and monitoring molecular signals in easily accessible biological samples that can diagnose or predict the risk of preterm labour (PTL) in pregnant women will reduce or prevent PTBs. A number of studies identified putative biomarkers for PTL including protein, miRNA and hormones from various body fluids. However, biomarkers identified from these studies usually lack consistency and reproducibility. Extracellular vesicles (EVs) in circulation have gained significant interest in recent years as these vesicles may be involved in cell-cell communication. We have used an improved small RNA library construction protocol and a newly developed size exclusion chromatography (SEC)-based EV purification method to gain a comprehensive view of circulating RNA in plasma and its distribution by analysing RNAs in whole plasma and EV-associated and EV-depleted plasma. We identified a number of miRNAs in EVs that can be used as biomarkers for PTL, and these miRNAs may reflect the pathological changes of the placenta during the development of PTL. To our knowledge, this is the first study to report a comprehensive picture of circulating RNA, including RNA in whole plasma, EV and EV-depleted plasma, in PTL and reveal the usefulness of EV-associated RNAs in disease diagnosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".