Could circRNA be a new biomarker for pre‐eclampsia?
Bibliographic record
Abstract
Pre-eclampsia is a devastating complication of pregnancy which is characterized by hypertension and proteinuria in pregnant women. Pre-eclampsia is important as it is the leading cause of death. Moreover, untreated pre-eclampsia might lead to other lethal complications, for both fetus and mother. Pre-eclampsia can also affect the quality of life in affected women. Despite a large number of risk factors for pre-eclampsia, these risk factors are able to detect just 30% of women who are susceptible to pre-eclampsia. Heterogeneous manifestations of pre-eclampsia necessitate the discovery of potential biomarkers required for its early detection. Circular RNAs (circRNAs) are a type of RNA which are more abundant, specific, and highly organized compared with other types of RNA. Accordingly, circRNAs have been suggested as one of the potential biomarkers for different diseases. Recently, researchers have shown interest in the effects of circRNAs in pre-eclampsia, although the current evidence is limited. The majority of obstetricians are probably not aware of circRNAs as a useful biomarker. Here, we aimed to summarize recent supporting evidence and assess the mechanisms by which circRNAs are involved in pre-eclampsia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".