Single blastomere removal from murine embryos is associated with activation of matrix metalloproteinases and Janus kinase/signal transducers and activators of transcription pathways of placental inflammation
Bibliographic record
Abstract
Single blastomere removal from cleavage-stage embryos, a common procedure used in conjunction with preimplantation genetic diagnosis (PGD), may affect reproductive outcomes. We hypothesized that negative pregnancy outcomes associated with PGD may be due to impairment of placental signaling pathways. The goal of this study was to determine the molecular mechanisms through which placental signaling is deregulated by blastomere removal. Four-cell stage murine embryos produced by in vitro fertilization were subjected to removal of a single blastomere (biopsied) or to the same manipulations without the blastomere removal (controls). Placental tissues from term (18.5 day) pregnancies obtained after embryo transfer were tested for levels of nitrosative species, interleukin 6, signal transducers and activators of transcription (STAT) 1 and 3, suppressors of cytokine signaling (SOCS) 1, 2 and 3 and matrix metalloproteinases (MMP) 1, 2, 3 and 9. Significant increases in nitrosative stress (P < 0.05), phosphorylative activation of STAT1 (P < 0.05) but not STAT3, lower levels of the inhibitors SOCS2 (P < 0.01) and SOCS3 (P < 0.001) and activation of MMP9 (P < 0.001) were observed in placentas derived from biopsied embryos, compared with controls. Such effects could contribute to greater levels of premature membrane rupture, incorrect parturition, preterm birth and intrauterine growth restriction associated with PGD. This work has determined signaling mechanisms that may be responsible for blastomere removal effects on placental function, with the potential to become targets for improving obstetric and neonatal outcomes in assisted reproduction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".