Monocytes adhering by LFA-1 to placental syncytiotrophoblasts induce local apoptosis via release of TNF-α. A model for hematogenous initiation of placental inflammations
Bibliographic record
Abstract
Abstract Placental inflammations (villitis) are accompanied by loss of the syncytiotrophoblast, which is the cellular barrier separating maternal blood from fetal tissue in the villous placenta. We propose that syncytiotrophoblast loss is mediated by adhesion of activated maternal monocytes. This hypothesis was tested with a co-culture model of peripheral blood monocytes and placental syncytiotrophoblasts. We find that LPS-activated monocytes adhere to interferon-γ (IFN-γ)-treated syncytiotrophoblasts via monocyte LFA-1 for >48 h, during which time the monocytes induce trophoblast apoptosis and subsequent damage of the trophoblast layer. Optimal monocyte-mediated syncytiotrophoblast death requires both lipopolysaccharide (LPS) and IFN-γ and is inhibited by either anti-tumor necrosis factor (TNF) antibody or epidermal growth factor. Syncytiotrophoblast damage is largely limited to culture surfaces in the vicinity of bound monocytes. These results show that activated maternal monocytes bound to the placental barrier can induce focal damage mediated by the inflammatory cytokine TNF-α and suggest a route for maternal leukocyte infiltration into the fetal stroma.
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 | 0.000 |
| 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 | 0.000 |
| 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".