The Density of Cell Nuclei at the Materno-Fetal Exchange Barrier is Sexually Dimorphic in Normal Placentas, but not in IUGR
Bibliographic record
Abstract
Placental sexual dimorphism is of special interest in prenatal programming. Various postnatal diseases with gender dependent incidence, especially neuropsychiatric disorders like schizophrenia and autism spectrum disorders, have prenatal risk factors established. However, the functional relevance of placental microarchitecture in prenatal programming is poorly investigated, mainly due to a lack of statistically efficient methods. We hypothesized that the recently established 3D microscopic analysis of villous trees would be able to identify microscopic structural correlates of human placental sexual dimorphism. We analyzed the density of cell nuclei of villous trophoblast, i.e. the materno-fetal exchange barrier, in placentas from term pregnancies. The cell nuclei were grouped into proliferative and non-proliferative nuclei by detection of a proliferation marker (PCNA). Normal female placentas showed a higher density of non-proliferating nuclei (PCNA-negative) in villous trophoblast than normal male placentas. The density of PCNA-negative cell nuclei was higher in placentas of pregnancies with intrauterine growth retardation (IUGR) than in control placentas. The data of the present study shows that the density of non-proliferative cell nuclei in the syncytial layer of villous trophoblast is influenced by fetal sex and by IUGR, while proliferation remains unchanged. A novel concept of post-fusion regulation of syncytial structure and function is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".