Placental microRNAs in pregnancies with early onset intrauterine growth restriction and preeclampsia: potential impact on gene expression and pathophysiology
Bibliographic record
Abstract
BACKGROUND: A normally developed placenta is integral to a successful pregnancy. Preeclampsia (PE) and intrauterine growth restriction (IUGR) are two common pregnancy related complications that maybe a result of abnormal placental development. Placental microRNAs (miRNAs) have been investigated as potential biomarkers for these complications, as they may play a role in placental development and pathophysiology by influencing gene expression. The purpose of this study is to utilize next-generation sequencing to determine miRNA and gene expression in human placental (chorionic villous) samples from three distinct patient groups with early-onset (EO) PE, IUGR, or PE + IUGR. METHODS: Placental tissues were collected from four patient groups (control [N = 21], EO-PE [N = 20], EO-IUGR [N = 18], and EO-PE + IUGR [N = 20]), and total RNA was used for miRNA and RNA sequencing on the Illumina Hiseq2000 platform. For stringent differential expression analysis multiple analysis programs were used to analyze both expression datasets in each patient group compared to gestational age-matched controls. RESULTS: Analysis revealed miRNAs and genes that are disease-specific, as well as others that were common between disease groups, which suggests common underlying placental pathologies in EO-PE and EO-IUGR. More specifically, 6 miRNAs and 22 genes were identified to be differentially expressed in all three patient groups. In addition, integrative analysis between the miRNA and gene expression datasets revealed candidate gene targets for miRNAs of interest. CONCLUSIONS: Integration of miRNA and RNA profiling in the same three subgroups of pregnancy complications, provides an alternate level of molecular information, in addition it can be used to better understand both unique and common molecular mechanisms involved in the pathophysiology of these diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".