Immune Dysregulation and Trophoblastic Dysfunction as a Potential Cause of Idiopathic Recurrent Pregnancy Loss
Bibliographic record
Abstract
Recurrent pregnancy loss (RPL) is a multifactorial condition affecting 1-5% of couples, often with unclear etiology. Idiopathic pregnancy losses (iPLs) are particularly challenging due to unknown molecular mechanisms. This study investigates the transcriptomic profiles of first-trimester products of conception (POC) from iPLs to uncover underlying molecular pathways. We performed RNA-sequencing on nine POC samples, identifying two distinct clusters enriched in trophoblast and decidual cells. Deconvolution analysis revealed reduced syncytiotrophoblast (STB) cells, with increased cytotrophoblast (CTB) and extravillous trophoblast (EVT) cells in iPLs. Gene Set Enrichment Analysis highlighted immune pathways enrichment in both villous trophoblasts and decidua. Gene ontology (GO) analysis of downregulated genes implicated hormonal and endocrine processes, consistent with STB reduction, while upregulated genes were associated with MHC protein complex and immune system processes, aligning with EVT increases. Histological analysis showed chronic histiocytic intervillositis (CHI) in iPL samples, supporting maternal immune dysregulation in unexplained RPL. Together, transcriptomic and histological analyses indicate that immune signaling dysregulation and impaired trophoblast differentiation may underlie unexplained iPLs. These findings bridge molecular and histopathological evidence, underscoring the interplay between trophoblast dysfunction and immune imbalance. Our results provide insights into iPL pathogenesis, highlighting potential biomarkers that may contribute to improved diagnosis and future research.
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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 | 0.001 | 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".