Adverse neonatal outcomes are associated with a sex-specific placental inflammatory profile
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The placenta is crucial for fetal development; altered function is associated with complications and adverse neonatal outcomes. Our goal was to ascertain changes in the placental transcriptome in relation to neonatal outcome and fetal/placental sex. STUDY DESIGN: Seventy-two mother-baby dyads were included. Demographic, obstetrical, neonatal, and infant health data were obtained through medical charts. Adverse neonatal outcome was defined as the presence of a pulmonary, cardiac, neurological or other health complication. Bulk RNA-sequencing of placental biopsies was obtained. P-value <0.05 was considered statistically significant. RESULTS: Neonates experiencing adverse outcomes were more likely to be premature or have lower birth weights. Analysis of the placental transcriptome revealed a predominant inflammatory profile in pregnancies associated with adverse neonatal outcomes with the top pathways being related to immune and inflammatory responses. Among differently expressed genes (DEGs), 1237 were upregulated and 239 were downregulated in adverse vs no adverse outcomes. Furthermore, sex-specific differences in gene expression were observed and indicated that male and female placentas displayed unique DEGs in association with adverse outcomes. Indeed, no DEG was observed in female placentas when comparing those without vs with adverse neonatal outcomes, as opposed to 1279 DEGs in male placentas, of which 91% were up-regulated in adverse subgroups. CONCLUSIONS: These findings highlight that inflammatory pathways are upregulated in placentas in association with adverse neonatal outcomes, and showcase the importance of the fetal sex in understanding neonatal health. The placenta provides a unique tool for early identification of high-risk infants rapidly after delivery.
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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 | 0.001 |
| 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) | 0.002 | 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".